Books

1. Yalcin M.E., Suykens J.A.K., Vandewalle J.P.L., Cellular Neural Networks, Multi-Scroll Chaos and Synchronization, vol. 50 of World Scientific Series on Nonlinear Science, Series A, World Scientific Pub. Co (Singapore, ISBN 981-256-161-7), 2005, 248 p.


2. Suykens J.A.K., Van Gestel T., De Brabanter J., De Moor B., Vandewalle J., Least Squares Support Vector Machines, World Scientific Publishing Co., Pte, Ltd. (Singapore), (ISBN : 981-238-151-1), 2002.


3. Suykens J.A.K., Vandewalle J., De Moor B., Artificial Neural Networks for Modeling and Control of Non-Linear Systems, Kluwer Academic Publishers, 1995, 235 p.

Edited Books

1. Suykens J.A.K., Signoretto M., Argyriou A., (eds.), Regularization, Optimization, Kernels, and Support Vector Machines, Machine Learning & Pattern Recognition series, Chapman & Hall/CRC (Boca Raton , USA), 2014, 525 p.


2. Suykens J.A.K., Argyriou A., De Brabanter K., Diehl M., Pelckmans K., Signoretto M., Van Belle V., Vandewalle J., (eds.), International workshop on advances in Regularization, Optimization, Kernel Methods and Support Vector Machines: theory and applications (ROKS 2013), Book of Abstracts, KU Leuven (Leuven, Belgium), 2013, 128 p.


3. De Schutter J., Diehl M., Glineur F., Jarlebring E., Louveaux Q., Michiels W., Smets I., Suykens J., Swevers J., Vandewalle J., Van Impe J., Verschuure M., (eds.), Book of Abstracts of the 14th Belgian-French-German Conference on Optimization, Proc of the Belgian-French-German Conference on Optimization, 14th edition, KU Leuven (Leuven, Belgium), 2009, 267 p.


4. Suykens J.A.K., Horvath G., Basu S., Micchelli C., Vandewalle J., (eds.), Advances in Learning Theory : Methods, Models and Applications, vol. 190 of NATO-ASI Series III : Computer and Systems Sciences, IOS Press (Amsterdam, The Netherlands), (ISBN 1-58603-341-7), 2003, 436 p.


5. Suykens J.A.K., Vandewalle J., (eds.), Nonlinear Modeling: advanced black-box techniques, Kluwer Academic Publishers, 1998, 256 p.

Contributions to books

1. Orchel M., Suykens J.A.K., "Axiomatic Kernels on Graphs for Support Vector Machines", in Artificial Neural Networks and Machine Learning, ICANN 2019: Workshop and Special Sessions. ICANN 2019, (Tetko I., Kurkova V., Karpov P., and Theis F., eds.), vol. 11731 of Lecture Notes in Computer Science, Springer, 2019, pp. 685-700.


2. Langone R., Jumutc V., Suykens J. A. K., "Large-Scale Clustering Algorithms", in Chapter 1 of Data Science and Big Data: An Environment of Computational Intelligence, (Pedrycz W., and and Chen S.-M., eds.), vol. 24 of Studies in Big Data, Springer International Publishing, 2017, pp. 3-28.


3. Yang Y., Feng Y., Suykens J.A.K., "Robust Matrix Completion through Nonconvex Approaches and Efficient Algorithms", in Chapter 8 Handbook of Robust Low-Rank and Sparse Matrix Decomposition, (Bouwmans T., Serhat Aybat N., and Zahzah E., eds.), Chapmann and Hall (London, UK), 2016, pp. 210 -228.


4. Langone R., Mall R., Alzate C., Suykens J.A.K., "Kernel Spectral Clustering and applications", in Chapter 6 of Unsupervised Learning Algorithms, (Celebi M. E., and Aydin K., eds.), Springer International Publishing Switzerland, 2016, pp. 135-161.


5. Langone R., Mall R., Vandewalle J., Suykens J. A. K., "Discovering cluster dynamics using kernel spectral methods", in Chapter 1 of Complex Systems and Networks, (Jinhu L., Xinghuo Y., Guanrong C., and and Wenwu Y., eds.), vol. 2 of mph{The Springer Series in Understanding Complex Systems}, Springer-Verlag Berlin, 2016, pp. 1-24.


6. Falck T., De Moor B., Suykens J.A.K., "Kernel Based Identification of Systems with Multiple Outputs using Nuclear Norm Regularization", in Chapter 17 in Regularization, Optimization, Kernels, and Support Vector Machines, (Suykens J.A.K., Signoretto M., and Argyriou A., eds.), Chapman & Hall/CRC (Boca Raton, USA), 2014, pp. 387-415.


7. Argyriou A., Signoretto M., Suykens J.A.K., "Hybrid Algorithms with Applications to Sparse and Low Rank Regularization", in Chapter 3 in Regularization, Optimization, Kernels, and Support Vector Machines, (Suykens J.A.K., Signoretto M., and Argyriou A., eds.), Chapman & Hall/CRC (Boca Raton , USA), 2014, pp. 55-85.


8. Mall R., Suykens J.A.K., "KSC-net: Community Detection for Big Data Networks", in Big Data: Algorithms, Analytics, and Applications, (Li K., Jiang H., Yang L., and Cuzzocrea A., eds.), CRC Press, Taylor & Francis Group. (Boca Raton, USA), 2014, pp. 1-20.


9. Suykens J.A.K., "Gedrag in complexe netwerken", in Chapter 9 of Onweerstaanbaar veranderlijk, Over de cultuur van de wetenschap, ( Tollebeek J., Verstraete J.C., and van de Perre E., eds.), Leuven University Press (Leuven, Belgium), 2013, pp. 60-63.


10. Suykens J.A.K., "Introduction to Machine Learning", in Chapter 13 of Academic Press Library in Signal Processing, ( Theodoridis S., and Chellappa R., eds.), vol. 1 of Signal Processing Theory and Machine Learning, Academic press (Waltham, USA), 2014, pp. 765-774.


11. Signoretto M., Suykens J.A.K., "Kernel Methods", in Chapter 32 (Part D) of Handbook of Computational Intelligence, (Kacprzyk J., and Pedrycz W., eds.), Springer, 2015, pp. 577-605.


12. Necoara I., Dumitrache I., Suykens J.A.K., "Smoothing Techniques-Based Distributed Model Predictive Control Algorithms for Networks", in Chapter in Time Delay Systems: Methods, Applications and New Trends, (Sipahi R., Vyhlidal T., Niculescu S.-I., and Pepe P., eds.), vol. 423 of Lecture Notes in Control and Information Sciences, Springer, 2012, pp. 307-318.


13. Luts J., Laudadio T., Idema A.J., Simonetti A.W., Heerschap A., Vandermeulen D., Suykens J.A.K., Van Huffel S., "Nosologic imaging of brain tumors using MRI and MRSI", in Chapter 16 Brain tumors (part 1), (Hayat M.A., ed.), vol. 3 of mph{Tumors of the central nervous system}, Springer, 2011, pp. 155-168.


14. Goethals I., Pelckmans K., Falck T., Suykens J.A.K., De Moor B., "NARX Identification of Hammerstein Systems using Least-Squares Support Vector Machines", in Chapter 15 of Block-oriented Nonlinear System Identification, (Giri F., and Bai E.-W., eds.), vol. 404 of Lecture notes in control and information sciences, Springer, 2010, pp. 241-256.


15. Suykens J.A.K., "Kernel Methods", in Chapter in Comprehensive Chemometrics, volume 3, (Brown S., Tauler R., and Walczak R., eds.), Elsevier, 2009, pp. 437-451.


16. Pochet N.L.M.M., Ojeda F., De Smet F., De Bie T., Suykens J.A.K., De Moor B.L.R., "Kernel clustering for knowledge discovery in clinical microarray data analysis", in Chapter 3 Kernel methods in bioengineering, communications and image processing, (Camps-Valls G., Rojo-Alvarez J.L., and Martinez-Ramon M., eds.), Idea Group Inc (Hershey, Pennsylvania), 2005, pp. 64-92.


17. Pelckmans K., Goethals I., De Brabanter J., Suykens J.A.K., De Moor B., "Componentwise Least Squares Support Vector Machines", in Chapter Support Vector Machines: Theory and Applications, (Wang L., ed.), Springer, 2005, pp. 77-98.


18. Lu C., Suykens J.A.K., Timmerman D., Vergote I., Van Huffel S., "Linear and Nonlinear Preoperative Classification of Ovarian Tumors", in Chapter 11 of Knowledge Based Intelligent System for Health Care, (Ichimura T. and Yoshida K. , ed.), vol. 7 of International Series on Advanced Intelligence, Advanced Knowledge International (Magill, Australia), 2004, pp. 343-382.


19. Suykens J.A.K., Van Gestel T., De Brabanter J., De Moor B., Vandewalle J., "Support Vector Machines : Least Squares Approaches and Extensions", in Chapter 8 of Advances in Learning Theory : Methods, Models and Applications, (Suykens J.A.K., Horvath G., Basu S., Micchelli C. and Vandewalle J., eds.), IOS Press (NATO-ASI Series in Computer and Systems Sciences) (Amsterdam, The Netherlands), 2003, pp. 155-178.


20. Suykens J.A.K.,Yalcin M., Vandewalle J., "Chaotic systems synchronization", in Chaos and Bifurcation Control : Theory and Applications, (Chen G., Yu X. and Hill D., eds), vol. 292 of Lecture notes in control and information sciences, Springer-Verlag, 2003, pp. 117-135.


21. Suykens J.A.K., Yalcin M.E., Vandewalle J., "A Generic Class of Chaotic and Hyperchaotic Circuits with Synchronization Methods", in Chapter 8 of Chaos and Bifurcations in Circuits and Systems, (Chen G., and Ueta T., eds.), vol. 11 of World Scientific Series on Nonlinear Science, Series B, World Scientific (Singapore, New Jersey), 2002, pp. 151-170.


22. Vandewalle J., Suykens J.A.K., De Moor B., "Non-linear circuit theory : from private playground for academic researchers to innovative information processing circuits", in Dedicated to Leon O. Chua on the occasion of this 60th birthday Ad Sexagintas Annos Prospere Celebrandos, (Phua K.K., ed.), World Scientific (Singapore), 1998, pp. 155-157.


23. Suykens J.A.K., Yang T., Vandewalle J., Chua L.O., "Impulsive Control and Synchronization of Chaos", in Chapter 13 of Controlling Chaos and Bifurcations in Engineering Systems, (Chen G., ed.), CRC Press (Boca Raton, USA), 1999, pp. 275-298.


24. Suykens J.A.K., Vandewalle J., "The K.U.Leuven time-series prediction competition", in Chapter 9 of Nonlinear Modeling: advanced black-box techniques, (Suykens J.A.K., and Vandewalle J., eds.), Kluwer Academic Publishers, 1998, pp. 241-253.


25. Tan S., Suykens J.A.K., Yu Y., Vandewalle J., "Nonlinear System Modeling", in Chapter of Control and Dynamic Systems, (Leondes C.T., ed.), vol. 7 of Neural Network Systems Techniques and Applications, Academic Press (London, United Kingdom), 1998, pp. 383-433.

International Journal Papers

1. Kontras K., Chatzichchristos C., Phan H., Suykens J., De Vos M., "CoRe-Sleep : A multimodal fusion Framework for Time Series Robust to Imperfect Modalities", IEEE Tranactions on Neural Systems and Rehabilitation Engineering, vol. 32, 2024, pp. 840-849.


2. Tonin F., Tao Q., Patrinos P., Suykens J.A.K., "Deep Kernel Principal Component Analysis for Multi-level Feature Learning", Neural Networks, vol. 170, Feb. 2024, pp. 578-595.


3. Pandey A., De Meulemeester H., De Moor B., Suykens J.A.K., "Multi-view Kernel PCA for Time series Forecasting", Neurocomputing, vol. 554, 2023, 126639 p.


4. Chen Y., Shen X., Liu Y., Tao Q., Suykens J.A.K., "Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer", Pattern Recognition Letters, vol. 166, Jan. 2023, pp. 53-60.


5. Tao Q., Li L., Huang X., Xi X., Wang S., Suykens J., "Piecewise Linear Neural Networks and Deep Learning", Nature Reviews Methods Primers, vol. 2, no. 1, Jun. 2022, 43 p.


6. Tao Q., Tonin F., Patrinos P., Suykens J., "Tensor-based Multi-view Spectral Clustering via Shared Latent Space", Information Fusion, vol. 108, Aug. 2024, pp. 1-15.


7. He M., He F., Shi L., Huang X., Suykens J.A.K., "Learning with Asymmetric Kernels: Least Squares and Feature Interpretation", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 8, Aug. 2023, pp. 10044-10054.


8. Tao Q., Li Z., Xu J., Lin S., De Schutter B., Suykens J.A.K., "Short-Term Traffic Flow Prediction Based on the Efficient Hinging Hyperplanes Neural Network", IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, Sep. 2022, pp. 15616-15628.


9. Peirelinck T., Kazmi H.S., Mbuwir B., Hermans C., Spiessens F., Suykens J.A.K., Deconinck G., "Transfer learning in demand response : a Review of algorithms for data-efficient modelling and control", Energy and AI, vol. 7, Jan. 2022, 100126 p.


10. Chen Y., Hu S.X., Shen X., Ai C., Suykens J.A.K., "Compressing Features for Learning with Noisy Labels", IEEE Transactions on Neural Networks and Learning Systems, vol.35, no. 2, Feb. 2024, pp. 2124-2138.


11. Xin M., Mei X., Suykens J.A.K., "A. novel neural grey system model with Bayesian regularization and its applications", Neurocomputing, vol. 456, 2021, pp. 61-75.


12. Tao Q., Xu J., Li Z., Xie N., Wang S., Li X., Suykens J., "Toward Deep Adaptive Hinging Hyperplanes", IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 11, Nov. 2022, pp. 6373-6387.


13. Theodorakos K., Agudelo M., Schreurs J., Suykens J.A.K., De Moor B., "Island Transpeciation: A Co-Evolutionary Neural Architecture Search, applied to country-scale air-quality forecasting", IEEE Transactions on Evolutionary Computation, vol. 27, no. 4, Aug. 2023, pp. 878-892.


14. Singaravel S., Suykens J.A.K., Janssen H., Geyer P., "Explainable deep convolutional learning for intuitive model development by non machine learning domain experts", Design Science, vol. 6, no. E23, 2020, pp. .


15. Tao Q., Li Z., Xu J., Xie N., Wang S., Suykens J.A.K., "Learning with continuous piecewise linear decision trees", Expert Systems with Applications, vol. 168, Apr. 2021, pp. 114214-.


16. Villalobos K., Suykens J.A.K., Illarramendi A., "A flexible alarm prediction system for smart manufacturirng scenarios following a forecaster-analyzer approach", Journal of Intelligent Manufacturing, vol. 32, 2021, pp. 1323-1344.


17. Tonin F., Patrinos P., Suykens J. A.K., "Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints", Neural Networks, vol. 142, Oct. 2021, pp. 661-679.


18. Fanuel M., Schreurs J., Suykens J.A.K., "Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems", SIAM Journal on Mathematics of Data Science, vol. 4, no. 3, 2022, pp. 1171-1190.


19. Schreurs J., Vranckx I., De Ketelaere B., Hubert M., Suykens J.A.K, Rousseeuw P.J., "Outlier detection in non-elliptical data by kernel MRCD", Statistics and Computing, vol. 31, Aug. 2021, pp. 1-18.


20. Pandey A., Fanuel M., Schreurs J., Suykens J. A. K., "Disentangled Representation Learning and Generation with Manifold Optimization", Neural Computation, vol. 34, no. 10, 2022, pp. 2009-2036.


21. Liu F., Shi L. Huang X., Yang J., Suykens J.A.K., "Analysis of Regularized Least Squares in Reproducing Kernel Krein Spaces", Machine Learning Journal, vol. 110, Feb. 2021, pp. 1145-1173.


22. Xu J., Tao Q., Li Z., Xi X., Suykens J.A.K., Wang S., "Efficient hinging hyperplanes neural network and its application in nonlinear system identification", Automatica, vol. 116, Jun. 2020, 108906 (art.nb) p.


23. Liu F., Huang X., Chen Y., Suykens J.A.K., "Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 10, Oct. 2022, pp. 7128-7148.


24. Fanuel M., Schreurs J., Suykens J.A.K., "Diversity sampling is an implicit regularization for kernel methods", SIAM Journal on Mathematics of Data Science (SIMODS), vol. 3, no. 1, Feb. 2021, pp. 280-297.


25. Liu F., Huang X., Chen Y., Suykens J.A.K., "Towards a Unifying Quadrature Framework for Large-Scale Kernel Machines", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 11, Nov. 2022, pp. 7975-7988.


26. Liu F., Huang X., Shi L., Yang J., Suykens J.A.K., "A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels", IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 8, Aug. 2020, pp. 2965-2979.


27. Singaravel S., Suykens J.A.K., Geyer P., "Deep convolutional learning for general early design stage prediction models", Advanced Engineering Informatics, vol. 42, Oct. 2019, pp. 100982-.


28. Fanuel M., Schreurs J., Suykens J.A.K., "Nystrom landmark sampling and regularized Christoffel functions", Machine Learning, vol. 11, 2022, pp. 2213-2254.


29. Pandey A., Schreurs J., Suykens J. A. K., "Generative Restricted Kernel Machines: A Framework for Multi-view Generation and Disentangled Feature Learning", Neural Networks, vol. 135, no. 0893-6080, Mar. 2021, pp. 177-191.


30. Liu F., Shi L., Huang X., Yang J., Suykens J.A.K., "Generalization Properties of hyper-RKHS and its Application to Out-of-Sample Extensions", Journal of Machine Learning Research, vol. 22, no 38, Jan. 2021, 38 p.


31. Kazmi H., Suykens J.A.K., Balint A., Driesen J., "Multi-agent reinforcement learning for modeling and control of thermostatically controlled loads", Applied Energy, vol. 238, Mar. 2019, pp. 1022-1035.


32. Singaravel S., Suykens J.A.K., Geyer P., "Deep-learning neural-network architectures and methods: using component-based models in building-design energy prediction", Advanced Engineering Informatics, vol. 38, Oct. 2018, pp. 81-90.


33. Mehrkanoon S., Suykens J.A.K., "Deep hybrid neural-kernel networks using random Fourier features", Neurocomputing, vol. 298, Jul. 2018, pp. 46-54.


34. Huang X., Shi L., Yan M., Suykens J.A.K., "Pinball loss minimization for one-bit compressive sensing: convex models and algorithms", Neurocomputing, vol. 314, no. 7, Nov. 2018, pp. 275-283.


35. Karevan Z., Suykens J.A.K., "Transductive LSTM for Time-Series Prediction: an Application to Weather Forecasting", Neural Networks, vol. 125, May 2020, pp. 1-9.


36. Houthuys L., Suykens J.A.K., "Tensor-based Restricted Kernel Machines for Multi-View Classification", Information Fusion, vol. 68, Apr. 2021, pp. 54-66.


37. Caicedo A., Varon C., Van Huffel S., Suykens J.A, "Functional Form Estimation using Oblique Projection Matrices for LS-SVM Regression Models", Plos One, vol. 14, no. 6, Jun. 2019, e0217967 p.


38. Yang Y., Feng Y., Suykens J.A.K., "Correntropy Based Matrix Completion", Entropy, Special Issue: Entropy in Signal Analysis, vol. 20, no. 3, Mar. 2018, pp. 1-23.


39. Fanuel M., Aspeel A., Delvenne J.C., Suykens J.A.K., "Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions", SIAM Journal on Mathematics of Data Science, vol. 4, no. 1, 2022, pp. 153-178.


40. Mehrkanoon S., Huang X., Suykens J.A.K., "Indefinite Kernel Spectral Learning", Pattern Recognition, vol. 78, Jun. 2018, pp. 144-153.


41. Karevan Z., Suykens J. A. K., "Transductive feature selection using clustering-based sample entropy for temperature prediction in weather forecasting", Entropy, vol. 20, no. 4, Apr. 2018, pp. 1-23.


42. Fang H., Huang X., Yang J., Suykens J.A.K., "Indefinite Kernel Logistic Regression with Concave-inexact-convex Procedure", IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 3, Mar. 2019, pp. 765 - 776.


43. Alaiz C.M, Suykens J.A.K., "Modified Frank-Wolfe Algorithm for enhanced sparsity in support vector machine classifiers", Neurocomputing, vol. 320, Dec. 2018, pp. 47-59.


44. Langone R., Suykens J.A.K., "Fast Kernel Spectral Clustering", Neurocomputing, vol. 268, Dec. 2017, pp. 27-33.


45. Houthuys L., Langone R., Suykens J.A.K., "Multi-view Least Squares Support Vector Machines Classification", Neurocomputing, vol. 282, Mar. 2018, pp. 78-88.


46. Bottegal G., Castro-Garcia R., Suykens J.A.K., "A two-experiment approach to Wiener system identification", Automatica, vol. 93, Jul. 2018, pp. 282-289.


47. Feng Y., Fan J., Suykens J.A.K., "A Statistical Learning Approach to Modal Regression", Journal of Machine Learning Research, vol. 21, no. 2, Feb. 2020, pp. 1-35.


48. Castro-Garcia R., Agudelo M., Suykens J.A.K., "Impulse Response Constrained LS-SVM modeling for MIMO Hammerstein System Identification", International Journal of Control, vol. 92, no. 4, sep 2017, pp. 908-925.


49. Chen Z., Batselier K., Suykens J.A.K., Wong N., "Parallelized Tensor Train Learning of Polynomial Classifiers", IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, Oct. 2018, pp. 4621-4632.


50. Houthuys L., Langone R., Suykens J.A.K., "Multi-View Kernel Spectral Clustering", Information Fusion, vol. 44, Nov. 2018, pp. 46-56.


51. Alaiz C.M., Fanuel M., Suykens J.A.K., "Robust Classification of Graph-Based Data", Data Mining and Knowledge Discovery, vol. 33, no. 1, Jan. 2019, pp. 230-251.


52. Gauthier B., Suykens J.A.K., "Optimal quadrature-sparsification for integral operator approximation", SIAM Journal on Scientific Computing, vol. 40, no. 5, 2018.


53. Mehrkanoon S., Suykens J.A.K., "Regularized Semipaired Kernel CCA for domain adaptation", IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 7, Jul. 2018, pp. 3199-3213.


54. Alaiz C.M., Fanuel M., Suykens J.A.K., "Convex Formulation for Kernel PCA and Its Use in Semisupervised Learning", IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 8, Aug. 2018, pp. 3863-3869.


55. Hang H., Steinwart I., Feng Y., Suykens J. A.K., "Kernel density estimation for dynamical systems", Journal of Machine Learning Research, vol. 19, no. *, Sep. 2018, pp. 1-49.


56. Fanuel M., Alaiz C.M., Fernandez A., Suykens J.A.K., "Magnetic Eigenmaps for the Visualization of Directed Networks", Applied and Computational Harmonic Analysis, vol. 44, no. 1, Jan. 2018, pp. 189-199.


57. Salzo S., Suykens J.A.K., "Generalized support vector regression: duality and tensor-kernel representation", Analysis and Applications, special issue on Mathematics of data science, vol. 18, no. 1, Jan. 2020, pp. 149-183.


58. Suykens J.A.K., "Deep Restricted Kernel Machines using Conjugate Feature Duality", Neural Computation, vol. 29, no. 8, Aug. 2017, pp. 2123-2163.


59. Fanuel M., Alaiz C. M., Suykens J.A.K., "Magnetic eigenmaps for community detection in directed networks", Physical Review E, vol. 95, no. 2, Feb. 2017, 022302 p.


60. Van Belle V., Van Calster B., Van Huffel S., Suykens J.A.K., Lisboa P., "Explaining support vector machines: a color based nomogram", Plos One, vol. 11, no. 10, Oct. 2016, pp. 1-33.


61. Mehrkanoon S., Suykens J.A.K., "Regularized Semipaired Kernel CCA for domain adaptation", IEEE Transactions on neural networks and learning systems, vol. 29, no. 7, Jul. 2018, pp. 3199-3213.


62. Shardt Y., Mehrkanoon S., Zhang K., Yang X., Suykens J.A.K., Ding S.X., Peng K., "Modelling the strip thickness in hot steel rolling mills using least-squares support vector machines", The Canadian Journal of Chemical Engineering, vol. 96, no. 1, Aug. 2018, pp. 171-178.


63. Hang H., Feng Y., Steinwart I., Suykens J., "Learning theory estimates with observations from general stationary stochastic processes", Neural Computation, vol. 28, No. 12, Dec. 2016, pp. 2853-2889.


64. Huang X., Maier A., Hornegger J., Suykens J.A.K., "Indefinite Kernels in Least Squares Support Vector Machines and Principal Component Analysis", Applied and Computational Harmonic Analysis, vol. 43, no. 1, Jul. 2017, pp. 162-172.


65. Huang X., Suykens J.A.K., Wang S., Maier A., Hornegger J., "Classification with Truncated l1 Distance Kernel", IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 5, May 2018, pp. 2025-2030.


66. Langone R., Van Barel M., Suykens J. A. K., "Efficient Evolutionary Spectral Clustering", Pattern Recognition Letters, vol. 84, Dec. 2016, pp. 78-84.


67. Heremans S., Suykens J.A.K., Van Orshoven J., "The effect of imposing 'fractional abundance constraints' onto the multilayer perceptron for sub-pixel land cover classification", International Journal of Applied Earth Observation and Geoinformation, vol. 44, Feb. 2016, pp. 226-238.


68. Feng Y., Yang Y., Huang X., Mehrkanoon S., Suykens J., "Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses", Neural Computation, vol. 28, no. 6, 2016, pp. 1217-1247.


69. Langone R., Van Barel M., Suykens J. A. K., "Entropy-based Incomplete Cholesky Decomposition for a Scalable Spectral Clustering Algorithm: Computational Studies and Sensitivity Analysis", Entropy, Special Issue on Information Theoretic Learning, vol. 18, no. 182, Jun. 2016, pp. 1-15.


70. Langone R., Reynders E., Mehrkanoon S., Suykens J. A. K., "Automated structural health monitoring based on adaptive kernel spectral clustering", Mechanical Systems and Signal Processing, vol. 90, Jun. 2017, pp. 64-78.


71. Feng Y., Lv S., Hang H., Suykens J. A.K., "Kernelized Elastic Net Regularization: Generalization Bounds and Sparse Recovery", Neural Computation, vol. 28, no. 3, Mar. 2016, pp. 525-562.


72. Castro-Garcia R., Tiels K., Agudelo M., Suykens J.A.K., "Hammerstein system identification through best linear approximation inversion and regularisation", International Journal of Control, vol. 91, no. 8, Jan. 2018, pp. 1757-1773.


73. Fanuel M., Suykens J.A.K., "Deformed Laplacians and spectral ranking in directed networks", Applied and Computational Harmonic Analysis, vol. 47, no. 2, Sep. 2019, pp. 397-422.


74. Frandi E., Nanculef R., Lodi S., Sartori C., Suykens J.A.K., "Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee", Machine Learning, vol. 104, no. 2-3, Sep. 2016, pp. 195-221.


75. Suykens J.A.K., "SVD revisited: a new variational principle, compatible feature maps and nonlinear extensions", Applied and Computational Harmonic Analysis, vol. 40, no. 3, May 2016, pp. 600-609.


76. Langone R., Suykens J. A. K., "Supervised aggregated feature learning for multiple instance classification", Information Sciences, vol. 375, Jan. 2017, pp. 234-245.


77. Jumutc V., Suykens J.A.K., "Reweighted Stochastic Learning", Neurocomputing, Special issue on Advances in Neural Networks, Intelligent Control and Information Processing, vol. 198, Jul. 2016, pp. 135-147.


78. Mehrkanoon S., Shardt Y.A.W., Suykens J.A.K., Ding S.X., "Estimating the Unknown Time Delay in Chemical Processes", Engineering Applications of Artificial Intelligence, vol. 55, Oct. 2016, pp. 219-230.


79. Shang C., Yang F., Gao X., Huang X., Suykens J.A.K., Huang D., "Concurrent Monitoring of Operating Condition Deviations and Process Dynamics Anomalies With Slow Feature Analysis", AIChE Journal, vol. 61, no. 11, 2015, pp. 3666-3682.


80. Yang Y., Feng Y., Suykens J.A.K., "A Rank-One Tensor Updating Algorithm for Tensor Completion", IEEE Signal Processing Letters, vol. 22, no. 10, Oct. 2015, pp. 1633-1637.


81. Mall R., Langone R., Suykens J.A.K., "Netgram: Visualizing Communities in Evolving Networks", Plos One, Sep. 2015, pp. 1-24.


82. Xi X., Huang X., Suykens J.A.K., Wang S., "Coordinate Descent Algorithm for Ramp Loss Linear Programming Support Vector Machines", Neural Processing Letters, vol. 43, no. 3, Jun. 2016, pp. 887-903.


83. Li L., Huang X., Suykens J.A.K., "Signal Recovery for Jointly Sparse Vectors with Diff", Signal Processing, vol. 108, Mar. 2015, pp. 451-458.


84. Mehrkanoon S., Agudelo M., Suykens J.A.K, "Incremental multi-class semi-supervised clustering regularized by Kalman filtering", Neural Networks, vol. 71, Nov. 2015, pp. 88-104.


85. Shang C., Huang X., Suykens J., Huang D., "Enhancing Dynamic Soft Sensors based on DPLS: a Temporal Smoothness Regularization Approach", Journal of Process Control, vol. 28, Apr. 2015, pp. 17--26.


86. Huang X., Shi L., Suykens J.A.K., "Solution Path for pin-SVM Classifiers with Postive and Negative tau Values", IEEE Transactions on Neural Networks and Learning Systems, vol. 28, July. 2017, pp. 1584-1593.


87. Yang Y., Feng Y., Suykens J.A.K., "Robust Low Rank Tensor Recovery with Regularized Redescending M-Estimator", IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 9, Sep. 2016, pp. 1933-1946.


88. Mehrkanoon S., Huang X., Suykens J.A.K., "Non-parallel Support Vector Classifiers with Different Loss Functions", Neurocomputing, vol. 143, Nov. 2014, pp. 294-301.


89. Feng Y., Yang Y., Suykens J. A. K., "Robust gradient learning with applications", IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 4, Apr. 2016, pp. 822-835.


90. Mall R., Langone R., Suykens J.A.K., "Multilevel Hierarchical Kernel Spectral Clustering for Real-Life Large Scale Complex Networks", PLOS One, e99966, vol. 9, no. 6, Jun. 2014, pp. 1-18.


91. Claesen M., De Smet F., Suykens J., De Moor B., "A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models", Neurocomputing, Special Issue on Advances in Learning With Label Noise, vol. 160, Jul. 2015, pp. 73-84.


92. Huang X., Shi L., Suykens J.A.K., "Sequential Minimal Optimization for SVM with Pinball Loss", Neurocomputing, vol. 149, Feb. 2015, pp. 1596--1603.


93. Marconato A., Sjoberg J., Suykens J.A.K., Schoukens J., "Improved Initialization for Nonlinear State-Space Modeling", IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 4, Apr. 2014, pp. 972-980.


94. Yang Y., Feng Y., Huang X., Suykens J. A. K., "Rank-1 Tensor Properties with Applications to a Class of Tensor Optimization Problems", SIAM J. Optim., vol. 26, no. 1, Jan. 2016, pp. 171--196.


95. Feng Y., Huang X., Shi L., Yang Y., Suykens J.A.K., "Learning with the Maximum Correntropy Criterion Induced Losses for Regression", Journal of Machine Learning Research, vol. 16, May 2015, pp. 993-1034.


96. Varon C., Alzate C., Suykens J.A.K., "Noise Level Estimation for Model Selection in Kernel PCA Denoising", IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 11, Nov. 2015, pp. 2650-2663.


97. Mehrkanoon S., Alzate C., Mall R., Langone R., Suykens J.A.K., "Multi-class semi-supervised learning based upon kernel spectral clustering", IEEE Transactions on neural networks and learning systems, vol. 26, no. 4, Apr. 2015, pp. 720-733.


98. Langone R., Agudelo M., De Moor B., Suykens J. A. K., "Incremental kernel spectral clustering for online learning of non-stationary data", Neurocomputing, vol. 139, Sep. 2014, pp. 246--260.


99. Huang X., Shi L., Suykens J.A.K., "Ramp Loss Linear Programming Support Vector Machine", Journal of Machine Learning Research, vol. 15, Jun. 2014, pp. 2185-2211.


100. Huang X., Liu Y., Lei S., Van Huffel S., Suykens J.A.K., "Two-level l1 Minimization for Compressed Sensing", Signal Processing, vol. 108, Mar. 2015, pp. 459-475.


101. Langone R., Alzate C., De Ketelaere B., Vlasselaer J., Meert W., Suykens J.A.K., "LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines", Engineering Applications of Artificial Intelligence, vol. 37, Jan. 2015, pp. 268-278.


102. Suykens J.A.K., "Generating quantum-measurement probabilities from an optimality principle", Physical Review A, vol. 87, no. 5, May 2013, pp. 052134-.


103. Mall R., Mehrkanoon S., Suykens J.A.K., "Identifying Intervals for Hierarchical Clustering using the Gershgorin Circle Theorem", Pattern Recognition Letters, vol. 55, no. 1, Apr. 2015, pp. 1-7.


104. Claesen M., De Smet F., Suykens J., De Moor B., "EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines", Journal of Machine Learning Research, http://jmlr.org/papers/v15/claesen14a.html, vol. 15, Jan. 2014, pp. 141-145.


105. Mehrkanoon S., Mehrkanoon S.D., Suykens J.A.K., "Parameter estimation of delay differential equations: an integration-free LS-SVM approach", Communication in Nonlinear Science and Numerical Simulation, vol. 19, no. 4, Apr. 2014, pp. 830-841.


106. Huang X., Shi L., Pelckmans K., Suykens J.A.K., "Asymmetric nu-tube Support Vector Regression", Computational Statistics and Data Analysis, vol. 77, Sep. 2014, pp. 371-382.


107. Shi L., Huang X., Feng Y., Suykens J.A.K., "Sparse Kernel Regression with Coefficient-based lq-Regularization", Journal of Machine Learning Research, vol. 20, Oct. 2019, pp. 1-44.


108. Thomas M., De Brabanter K., Suykens J.A.K., De Moor B., "Predicting breast cancer using an expression values weighted clinical classifier", BMC Bioinformatics, vol. 15, no411, 2014, pp. 1-11.


109. Mall R., Langone R., Suykens J.A.K., "Kernel Spectral Clustering for Big Data Networks", Entropy, Special Issue: Big Data, vol. 15, no. 5, May 2013, pp. 1567-1586.


110. Mall R., Langone R., Suykens J.A.K., "FURS: Fast and Unique Representative Subset selection retaining large scale community structure", Social Network Analysis and Mining, vol. 3, no. 4, Oct. 2013, pp. 1075-1095.


111. Jumutc V., Suykens J.A.K., "Multi-Class Supervised Novelty Detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 12, Dec. 2014, pp. 2510 - 2523.


112. Mehrkanoon S., Suykens J.A.K., "Learning Solutions to Partial Differential Equations using LS-SVM", Neurocomputing, vol. 159, Mar. 2015, pp. 105-116.


113. Huang X., Shi L., Suykens J.A.K., "Asymmetric Least Squares Support Vector Machine Classifiers", Computational Statistics and Data Analysis, vol. 70, Feb. 2014, pp. 395-405.


114. Mall R., Suykens J.A.K., "Very Sparse LSSVM Reductions for Large Scale Data", IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 5, Mar. 2015, pp. 1086 - 1097.


115. Huang X., Shi L., Suykens J.A.K., "Support Vector Machine Classifier with Pinball Loss", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 5, May 2014, pp. 984--997.


116. Shi L., Huang X., Tian Z., Suykens J.A.K., "Quantile Regression with L1-regularization and Gaussian Kernels", Advances in Computational Mathematics, vol. 40, no. 2, Apr. 2014, pp. 517--551.


117. Huang X., Mehrkanoon S., Suykens J.A.K.., "Support Vector Machines with Piecewise Linear Feature Mapping", Neurocomputing, vol. 117, Oct. 2013, pp. 118-127.


118. Van Belle V., Neven P., Harvey V., Van Huffel S., Suykens J.A.K., Boyd S., "Risk group detection and survival function estimation for interval coded survival methods", Neurocomputing, vol. 112, Jul. 2013, pp. 200-210.


119. Langone R., Alzate C. and Suykens J. A. K., "Kernel Spectral Clustering with Memory Effect", Physica A, vol. 392, no. 10, May 2013, pp. 2588-2606.


120. Huang X., Matijas M., Suykens J.A.K., "Hinging Hyperplanes for Time-Series Segmentation", IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 8, Aug. 2013, pp. 1279-1291.


121. Lou X., Suykens J.A.K., "Hybrid Coupled Local Minimizers", IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 61, no. 2, Feb. 2014, pp. 542-551.


122. Matijas M., Suykens J.A.K., Krajcar S., "Load Forecasting using a Multivariate Meta-Learning System", Expert Systems with Applications, vol. 40, no. 11, Sep. 2013, pp. 4427-4437.


123. Yu S., Tranchevent L., Liu X., Glanzel W., Suykens J.A.K., De Moor B., Moreau Y., "Optimized data fusion for kernel k-means clustering", IEEE Transations on Pattern Analysis and Machine Intelligence, vol. 34, no. 5, May 2012, pp. 1031-1038.


124. Hunyadi B., Signoretto M., Van Huffel S., Suykens J.A.K., De Vos M., "Incorporating structural information from the multichannel EEG improves patient-specific seizure detection", Clinical Neurophysiology, vol. 123, no. 12, Dec. 2012, pp. 2352-2361.


125. Mehrkanoon S., Suykens J.A.K., "LS-SVM approximate solution to linear time varying descriptor systems", Automatica, vol. 48, no. 10, Oct. 2012, pp. 2502-2511.


126. Alzate C., Suykens J.A.K., "Hierarchical Kernel Spectral Clustering", Neural Networks, vol. 35, Nov. 2012, pp. 21-30.


127. Widjaja D., Varon C., Caicedo A., Suykens J.A.K., Van Huffel S., "Application of Kernel Principal Component Analysis for Single Lead ECG-Derived Respiration", IEEE Transactions on Biomedical Engineering, vol. 59, no. 4, Apr. 2012, pp. 1169-1176.


128. De Brabanter K., Suykens J.A.K., De Moor B., "Nonparametric Regression via StatLSSVM", Journal of Statistical Software, vol. 55, no. 2, Oct. 2013, pp. --.


129. Signoretto M., Tran Dinh Q., De Lathauwer L., Suykens J.A.K., "Learning with Tensors: a Framework Based on Convex Optimization and Spectral Regularization", Machine Learning, vol. 94, no. 3, Mar. 2014, pp. 303-351.


130. Mehrkanoon S., Falck T., Suykens J.A.K., "Approximate Solutions to Ordinary Differential Equations Using Least Squares Support Vector Machines", IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 9, Sep. 2012, pp. 1356-1367.


131. Lou X.Y., Suykens J.A.K., "Finding Communities in Weighted Networks Through Synchronization", Chaos, vol. 21, no. 4, Dec. 2011, pp. 043116-.


132. Falck T., Dreesen P., De Brabanter K., Pelckmans K., De Moor B., Suykens J.A.K., "Least-Squares Support Vector Machines for the Identification of Wiener-Hammerstein Systems", Control Engineering Practice, vol. 20, no. 11, Nov. 2012, pp. 1165-1174.


133. Signoretto M., Van De Plas R., De Moor B., Suykens J. A. K., "Tensor Versus Matrix Completion: a Comparison with Application to Spectral Data", IEEE Signal Processing Letters, vol. 18, no. 7, Jul. 2011, pp. 403 - 406.


134. Combaz A., Chumerin N., Manyakov N.V., Robben A., Suykens J.A.K., Van Hulle M.M., "Towards the detection of Error-Related Potentials and its integration in the context of a P300 Speller Brain-Computer Interface", Neurocomputing, vol. 80, Mar. 2012, pp. 73-82.


135. Signoretto M., Olivetti E., De Lathauwer L., Suykens J. A. K., "Classification of multichannel signals with cumulant-based kernels", IEEE Transactions on Signal Processing, vol. 60, no. 5, May 2012, pp. 2304 - 2314.


136. De Brabanter K., De Brabanter J., Suykens J.A.K., De Moor B., "Kernel Regression in the Presence of Correlated Errors", Journal of Machine Learning Research, vol. 12, Jun. 2011, pp. 1955 - 1976.


137. Varon C., Alzate C., Suykens J.A.K., Debosscher J., "Kernel Spectral Clustering of Time Series in the CoRoT Exoplanet Database", Astronomy & Astrophysics, vol. 531, no. A156, Jul. 2011, pp. 1-15.


138. Komarov M.A., Osipov G.V., Suykens J.A.K., "Metastable states and transient activity in ensembles of excitatory and inhibitory elements", EPL (Europhysics Letters), vol. 91, no. 2, Aug. 2010, pp. 20006-.


139. Lou X., Suykens J.A.K., "Stability of coupled local minimizers within the Lagrange programming network framework", IEEE Transactions on Circuits and Systems-I, vol. 60, no. 2, Feb. 2013, pp. 377-388.


140. Signoretto M., De Lathauwer L., Suykens J.A.K., "A Kernel-based Framework to Tensorial Data Analysis", Neural Networks, Selected Papers from ICANN 2010, vol. 24, no. 8, Oct. 2011, pp. 861-874.


141. Geebelen D., Geebelen K., Truyen E., Michiels S., Suykens J.A.K., Vandewalle J., Joosen W., "QoS Prediction for web service compositions using kernel-based quantile estimation with online adaptation of the constant offset", Information Sciences, vol. 268, no. *, 2014, pp. 397-424.


142. Luts J., Molenberghs G., Verbeke G., Van Huffel S., Suykens J.A.K., "A mixed effects least squares support vector machine model for classification of longitudinal data", Computational Statistics & Data Analysis, vol. 56, no. 3, Mar. 2012, pp. 611-628.


143. De Brabanter K., Karsmakers P., De Brabanter J., Suykens J.A.K., De Moor B., "Confidence Bands for Least Squares Support Vector Machine Classifiers: A Regression Approach", Pattern Recognition, vol. 45, no. 6, Jun. 2012, pp. 2280-2287.


144. Yu S., Liu X., Tranchevent L., Glanzel W., Suykens J.A.K., De Moor B., Moreau Y., "Optimized data fusion for K-means Laplacian Clustering", Bioinformatics, vol. 27, no. 1, Jan. 2011, pp. 118-126.


145. Van Belle V.M.C.A., Van Calster B., Timmerman D., Bourne T., Bottomley C., Valentin L., Neven P., Van Huffel S., Suykens J.A.K., Boyd S., "A Mathematical Model for Interpretable Clinical Decision Support with Applications in Gynecology", PLoS ONE, vol. 7, no. 3, Mar. 2012, pp. 1-10.


146. Pavlov E.A., Osipov G.V., Chan C.K., Suykens J.A.K., "Map-based model of the cardiac action potential", Physics Letters A, vol. 375, no. 32, Jul. 2011, pp. 2894-2902.


147. Alzate C., Suykens J.A.K., "Sparse Kernel Spectral Clustering Models for Large-Scale Data Analysis", Neurocomputing, Special Issue on Advances in Artificial Neural Networks, Machine Learning and Computational Intelligence (ESANN 2010), vol. 74, no. 9, Apr. 2011, pp. 1382-1390.


148. De Brabanter K., De Brabanter J., Suykens J.A.K., De Moor B., "Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression", IEEE Transactions on Neural Networks, vol. 22, no. 1, Jan. 2011, pp. 110-120.


149. Geebelen D., Suykens J.A.K., Vandewalle J., "Reducing the Number of Support Vectors of SVM Classifiers Using the Smoothed Separable Case Approximation", IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 4, Apr. 2012, pp. 682 - 688.


150. Karsmakers P., Pelckmans K., De Brabanter K., Van hamme H., Suykens J.A.K., "Sparse Conjugate Directions Pursuit with Application to Fixed-size Kernel Models", Machine Learning, Special Issue on Model Selection and Optimization in Machine Learning, vol. 85, no. 1, Oct. 2011, pp. 109-148.


151. Van Belle V., Pelckmans K., Van Huffel S., Suykens J.A.K., "Improved Performance on High-Dimensional Survival Data using Survival-SVM", Bioinformatics, vol. 27, no. 1, Jan. 2011, pp. 87-94.


152. Postma G., Luts J., Idema A.J., Julia-Sape M., Moreno-Torres A., Gajewicz W., Suykens J.A.K., Heerschap A., Van Huffel S., Buydens L.M.C., "On the relevance of automatically selected single voxel MRS and multimodal MRI and MRSI features for brain tumor differentiation", Computers in Biology and Medicine, vol. 41, no. 2, Feb. 2011, pp. 87-97.


153. Luts J., Ojeda F., Van de Plas R., De Moor B., Van Huffel S., Suykens J.A.K, "A tutorial on support vector machine-based methods for classification problems in chemometrics", Analytica Chimica Acta, vol. 665, no. 2, Apr. 2010, pp. 129-145.


154. Van Belle V., Pelckmans K., Van Huffel S., Suykens J.A.K., "Support vector methods for survival analysis: a comparison between ranking and regression approaches", Artificial Intelligence in Medicine, vol. 53, no. 2, Oct. 2011, pp. 107-118.


155. Daemen A., Signoretto M., Gevaert O., Suykens JAK., De Moor B., "Improved microarray-based decision support with graph encoded interactome data", PLoS ONE, vol. 5, no. 4, Apr. 2010, pp. 1-16.


156. Yu S., Falck T., Daemen A., Tranchevent L.C., Suykens J.A.K., De Moor B., Moreau Y., "L2-norm multiple kernel learning and its application to biomedical data fusion", BMC Bioinformatics, vol. 11, no. 309, Jun. 2010, pp. 1-53.


157. Lopez J., Suykens J.A.K., "First and Second Order SMO Algorithms for LS-SVM Classifiers", Neural Processing Letters, vol. 33, no. 1, Feb. 2011, pp. 31-44.


158. Sahhaf S., De Brabanter K., Degraeve R., Suykens J.A.K., De Moor B., Groeseneken G., "Modelling of Charge Trapping/De-trapping Induced Voltage Instability in High-k Gate Dielectrics", IEEE Transactions on Device and Materials Reliability, vol. 12, no. 1, Mar. 2012, pp. 152-157.


159. Suykens J.A.K., Alzate C., Pelckmans K., "Primal and dual model representations in kernel-based learning", Statistics Surveys, DOI: 10.1214/09-SS052, vol. 4, Aug. 2010, pp. 148-183.


160. Van Belle V., Pelckmans K., Suykens J.A.K., Van Huffel S., "Learning Transformation Models for Ranking and Survival Analysis", Journal of Machine Learning Research, vol. 12, Mar. 2011, pp. 819-862.


161. Tsiaflakis P., Necoara I., Suykens J.A.K., Moonen M., "Improved Dual Decomposition Based Optimization for DSL Dynamic Spectrum Management", IEEE Transactions on Signal Processing, vol. 58, no. 4, Apr. 2010, pp. 2230-2245.


162. Suykens J.A.K., "Extending Newton's law from nonlocal-in-time kinetic energy", Physics Letters A, vol. 373, no. 14, Mar. 2009, pp. 1201-1211.


163. Petrov V.S., Osipov G.V., Suykens J.A.K., "Influence of passive elements on the dynamics of oscillatory ensembles of cardiac cells", Physical Review E, vol. 79, no. 4, Apr. 2009, pp. 046219-.


164. Komarov M.A., Osipov G.V., Suykens J.A.K., Rabinovich M.I., "Numerical studies of slow rhythms emergence in neural microcircuits: Bifurcations and stability", Chaos, Focus Issue: Nonlinear dynamics in cognitive and neural systems, vol. 19, no. 1, Mar. 2009, pp. 015107-.


165. Komarov M.A., Osipov G.V., Suykens J.A.K., "Sequentially activated clusters in neural networks", Europhysics Letters, vol. 86, no. 6, Jun. 2009, pp. 60006-.


166. De Brabanter K., De Brabanter J., Suykens J.A.K., De Moor B., "Optimized Fixed-Size Kernel Models for Large Data Sets", Computational Statistics & Data Analysis, vol. 54, no. 6, Jun. 2010, pp. 1484-1504.


167. Suykens J.A.K., Osipov G.V., "Introduction to Focus Issue: Synchronization in Complex Networks", Chaos, Focus Issue: Synchronization in Complex Networks, vol. 18, no. 3, Sep. 2008, pp. 037101 -.


168. Garcia-Gomez J., Luts J., Julia-Sape M., Krooshof P., Tortajada S., Vicente J., Melssen W., Fuster-Garcia E., Olier I., Postma G., Monleon D., Moreno-Torres A., Pujol J., Candiota A.-P., Martinez-Bisbal M.C., Suykens J.A.K., Buydens L., Celda B., Van Huffel S., Arus C., Robles M., "Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy", Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 22, Feb. 2009, pp. 5-18.


169. Necoara I., Suykens J.A.K., "Interior-Point Lagrangian Decomposition Method for Separable Convex Optimization", Journal of Optimization Theory and Applications, vol. 143, no. 3, Dec. 2009, pp. 567-588.


170. Van Belle V., Pelckmans K., Suykens J.A.K., Van Huffel S., "Additive survival least squares support vector machines", Statistics in Medicine, vol. 29, no. 2, Jan. 2010, pp. 296 - 308.


171. Imsland L., Rossiter J.A., Pluymers B., Suykens J.A.K., "Robust triple mode MPC", International Journal of Control, vol. 81, no. 4, Apr. 2008, pp. 679-689.


172. Luts J., Laudadio T., Idema A.J., Simonetti A.W., Heerschap A., Vandermeulen D., Suykens J.A.K., Van Huffel S., "Nosologic imaging of the brain: segmentation and classification using MRI and MRSI", NMR in Biomedicine, vol. 22, no. 4, May 2009, pp. 374-390.


173. Komarov M.A., Osipov G.V., Suykens J.A.K., "Variety of Synchronous Regimes in Neuronal Ensembles", Chaos, Focus Issue: Synchronization in Complex Networks, vol. 18, no. 3, Sep. 2008, pp. 037121 -.


174. Belykh V.N., Osipov G.V., Petrov V.S., Suykens J.A.K., Vandewalle J., "Cluster synchronization in oscillatory networks", Chaos, Focus Issue: Synchronization in Complex Networks, vol. 18, no. 3, Sep. 2008, pp. 037106-.


175. Necoara I., Suykens J.A.K., "Application of a smoothing technique to decomposition in convex optimization", IEEE Transactions on Automatic Control, vol. 53, no. 11, Dec. 2008, pp. 2674-2679.


176. Daemen A., Gevaert O., Ojeda F., Debucquoy A., Suykens J.A.K., Sempoux C., Machiels J.-P., Haustermans K., De Moor B., "A kernel-based integration of genome-wide data for clinical decision support", Genome Medicine, vol. 1, no. 4, Apr. 2009, pp. 39.1-39.17.


177. Alzate C., Suykens J. A. K., "Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 2, Feb. 2010, pp. 335-347.


178. Xavier de Souza S., Suykens J.A.K., Vandewalle J., Bolle D., "Coupled Simulated Annealing", IEEE Transactions on Systems, Man, and Cybernetics - Part B, vol. 40, no. 2, Apr. 2010, pp. 320 - 335.


179. Ojeda F., Suykens J.A.K, De Moor B., "Low rank updated LS-SVM classifiers for fast variable selection", Neural Networks, Special Issue on Advances in Neural Networks Research: IJCNN 2007, vol. 21, no. 2-3, Mar.-Apr. 2008, pp. 437-449.


180. Alzate C., Suykens J. A. K., "A Regularized Kernel CCA Contrast Function for ICA", Neural Networks, Special Issue on Advances in Neural Networks Research: IJCNN 07, vol. 21, no. 2-3, Mar.-Apr. 2008, pp. 170-181.


181. Luts J., Poullet J.-B., Garcia-Gomez J.M., Heerschap A., Robles M., Suykens J.A.K., Van Huffel S., "The effect of feature extraction for brain tumour classification based on short echo time 1H MR spectra", Magnetic Resonance in Medicine, vol. 60, no. 2, Jul. 2008, pp. 288-298.


182. Debruyne M., Hubert M., Suykens J.A.K., "Model Selection in Kernel Based Regression using the Influence Function", Journal of Machine Learning Research, vol. 9, Oct. 2008, pp. 2377-2400.


183. Luts J., Heerschap A., Suykens J.A.K., Van Huffel S., "A combined MRI and MRSI based Multiclass System for Brain Tumour Recognition using LS-SVMs with Class Probabilities and Feature Selection", Artificial Intelligence in Medicine, vol. 40, no. 2, Jun. 2007, pp. 87-102.


184. Suykens J.A.K., "Data Visualization and Dimensionality Reduction using Kernel Maps with a Reference Point", IEEE Transactions on Neural Networks, vol. 19, no. 9, Sep. 2008, pp. 1501-1517.


185. Hillier D., Gunel S., Suykens J.A.K., Vandewalle J., "Partial synchronization in oscillator arrays with asymmetric coupling", International Journal of Bifurcation and chaos, vol. 17, no. 11, Nov. 2007, pp. 4177 - 4185.


186. Debruyne M., Christmann A., Hubert M., Suykens J.A.K., "Robustness of reweighted least squares kernel based regression", Journal of Multivariate Analysis, vol. 101, no. 2, Feb. 2010, pp. 447-463.


187. Pochet N.L.M.M., Suykens J.A.K., "Support vector machines versus logistic regression: improving prospective performance in clinical decision-making", Ultrasound in Obstetrics & Gynecology, Opinion, vol. 27, no. 6, Jun. 2006, pp. 607-608.


188. Van Calster B., Timmerman D., Lu C., Suykens J.A.K., Valentin L., Van Holsbeke C., Amant F., Vergote I., Van Huffel S., "Preoperative diagnosis of ovarian tumors using Bayesian kernel-based methods", Ultrasound in Obstetrics and Gynecology, vol. 29, no. 5, May 2007, pp. 496-504.


189. Espinoza M., Suykens J.A.K., Belmans R., De Moor B., "Electric Load Forecasting - Using kernel based modeling for nonlinear system identification", IEEE Control Systems Magazine, Special Issue on Applications of System Identification, vol. 27, no. 5, Oct. 2007, pp. 43-57.


190. Pelckmans K., De Brabanter J., Suykens J.A.K., De Moor B., "Least Conservative Support and Tolerance Tubes", IEEE Transactions on Information Theory, vol. 55, no. 8, Aug. 2009, pp. 3799-3806.


191. Pelckmans K., Suykens J.A.K., De Moor B., "A convex Approach to Validation-based Learning of the Regularization Constant", IEEE Transactions on Neural Networks, vol. 18, no. 3, May 2007, pp. 917-920.


192. Van Gestel T., Baesens B., Van Dijcke P., Suykens J.A.K., Garcia J. Alderweireld T., "Linear and non-linear credit scoring by combining logistic regression and support vector machines", Journal Of Credit Risk, vol. 1, no. 4, 2005, pp. 31-60.


193. Alzate C., Suykens J.A.K., "Kernel Component Analysis using an Epsilon Insensitive Robust Loss Function", IEEE Transactions on Neural Networks, vol. 19, no. 9, Sep. 2008, pp. 1583-1598.


194. Van Gestel T., Baesens B., Van Dijcke P., Garcia J., Suykens J.A.K., Vanthienen J., "A process model to develop an internal rating system: sovereing credit ratings", Decision Support Systems, vol. 42, no. 2, Nov. 2006, pp. 1131-1151.


195. Xavier de Souza S., Suykens J.A.K., Vandewalle J., "Learning of Spatiotemporal Behavior in Cellular Neural Networks", International Journal of Circuit Theory and Applications, Special Issue on CNN Technology (Part 1), vol. 34, no. 1, Jan. 2006, pp. 127-140.


196. Van Gorp T., De Smet F., Pochet N., Engelen K., Van Hummelen P., Suykens J.A.K., Marchal K., Amant F., Moreau Y., Timmerman D., De Moor B., Vergote I., "Predicting the clinical behavior of ovarian cancer from gene expression profiles", International Journal of Gynecological Cancer, 14th International meeting of the ESGO (ESGO 14), Istanbul, Turkey, vol. 15, no. S2, Sep. 2005, pp. 66.


197. Yalcin M.E., Suykens J.A.K., "Spatiotemporal pattern formation on the ACE16k CNN chip", International Journal of Bifurcation and Chaos, vol. 16, no. 5, 2006, pp. 1537-1546.


198. Pelckmans K., De Brabanter J., Suykens J.A.K, De Moor B., "Handling Missing Values in Support Vector Machine Classifiers", Neural Networks, vol. 18, 2005, pp. 684-692.


199. Hoegaerts L., De Lathauwer L., Goethals I., Suykens J.A.K., Vandewalle J., De Moor B., "Efficiently Updating and Tracking the Dominant Kernel Principal Components", Neural Networks, vol. 20, no. 2, Mar. 2007, pp. 220-229.


200. Pochet N.L.M.M., Janssens F.A.L., De Smet F., Marchal K., Suykens J.A.K., De Moor B.L.R., "M@CBETH: a microarray classification benchmarking tool", Bioinformatics, vol. 21, no. 14, Jul. 2005, pp. 3185-3186.


201. Lu C., Devos A., Suykens J.A.K., Arus C., Van Huffel S., "Bagging linear sparse Bayesian learning models for variable selection in cancer diagnosis", IEEE Transactions on information technology in biomedicine, vol. 11, no. 3, May 2007, pp. 338-347.


202. Pluymers B., Roobrouck L., Buijs J., Suykens J.A.K., De Moor B., "Constrained Linear MPC with Time-Varying Terminal Cost using Convex Combinations", Automatica, vol. 41, no. 5, May 2005, pp. 831-837.


203. Van Gestel T., Espinoza M., Baesens B., Suykens J.A.K, Brasseur C., De Moor B., "A Bayesian Nonlinear Support Vector Machine Error Correction Model", Journal of Forecasting, vol. 25, no. 2, Mar. 2006, pp. 77-100.


204. Pelckmans K., Suykens J.A.K., De Moor B., "Building Sparse Representations and Structure Determination on LS-SVM Substrates", Neurocomputing, Special Issue, vol. 64, Mar. 2005, pp. 137-159.


205. Goethals I., Pelckmans K., Suykens J.A.K., De Moor B., "Subspace Identification of Hammerstein Systems using Least Squares Support Vector Machines", IEEE Transactions on Automatic Control, Special Issue on System Identification, vol. 50, no. 10, Oct. 2005, pp. 1509- 1519.


206. Espinoza M., Suykens J.A.K., De Moor B., "Kernel Based Partially Linear Models and Nonlinear Identification", IEEE Transactions on Automatic Control, Special Issue on System Identification, vol. 50, no. 10, Oct. 2005, pp. 1602-1606.


207. Pelckmans K., Espinoza M., De Brabanter J., Suykens J.A.K., De Moor B., "Primal-Dual Monotone Kernel Regression", Neural Processing Letters, vol. 22, no. 2, Oct. 2005, pp. pp. 171-182.


208. Pluymers B., Suykens J.A.K., De Moor B., "Min-Max feedback MPC using a time-varying terminal constraint set and comments on 'efficient robust constrained model predictive control with a time-varying terminal constraint set'", Systems and Control Letters, vol. 54, no. 12, Dec. 2005, pp. 1143-1148.


209. Goethals I., Pelckmans K., Suykens J.A.K., De Moor B., "Identification of MIMO Hammerstein Models using Least Squares Support Vector Machines", Automatica, vol. 41, no. 7, Jul. 2005, pp. 1263-1272.


210. Pelckmans K., De Brabanter J., Suykens J.A.K., De Moor B., "The differogram : Nonparametric noise variance estimation and its use for model", Neurocomputing, Special Issue on Signal Processing, vol. 69, no. 1-3, Dec. 2005, pp. 100-122.


211. Pelckmans K., Suykens J.A.K., De Moor B., "Additive regularization Trade-off : Fusion of Training and Validation levels in Kernel Methods", Machine Learning, vol. 62, no. 3, Mar. 2006, pp. 217-252.


212. Devos A., Simonetti A.W., van der Graaf M., Lukas L., Suykens J.A.K., Vanhamme L., Buydens L.M.C., Heerschap A., Van Huffel S., "The use of multivariate MR Imaging Intensities versus metabolic data from MR Spectroscopic Imaging for MR spectroscopic imaging for brain tumour classification", Journal of Magnetic Resonance, vol. 173, no. 2, Apr. 2005, pp. 218-228.


213. Pochet N., De Smet F., Suykens J.A.K., De Moor B., "Systematic benchmarking of micorarray data classification : assessing the role of nonlinearity and dimensionality reduction", Bioinformatics, vol. 20, no. 17, Nov. 2004, pp. 3185-3195.


214. Hoegaerts L., Suykens J.A.K., Vandewalle J., De Moor B., "Subset based least squares subspace regression in RKHS", Neurocomputing, vol. 63, Jan. 2005, pp. 293-323.


215. Devos A., Lukas L., Suykens J.A.K., Vanhamme L., Tate A.R., Howe F.A., Majos C., Moreno-Torres A., Van der Graaf M., Arus C., Van Huffel S., "Classification of brain tumours using short echo time 1H MRS spectra", Journal of Magnetic Resonance, vol. 170 , no. 1, Sep. 2004, pp. 164-175.


216. Xavier de Souza S., Yalcin M.E., Suykens J.A.K., Vandewalle J., "Toward CNN Chip-specific robustness", IEEE Transactions on Circuits and Systems I : Regular Papers, Special Issue on CNN Technology and Active Wave Computing, vol. 51, no. 5, May 2004, pp. 892 - 902.


217. Espinoza M., Suykens J.A.K., De Moor B., "Fixed-Size Least Squares Support Vector Machines : A large Scale application in electrical load forecasting", Computational Management Science , Special Issue on Support Vector Machines, vol. 3, no. 2, Apr. 2006, pp. 113-129.


218. Yalcin M.E., Suykens J.A.K., Vandewalle J., "True Random Bit Generation from a Double Scroll Attractor", IEEE Transactions on Circuits and Systems I : Fundamental Theory and Applications, vol. 51, no. 7, Jul. 2004, pp. 1395-1404.


219. Lukas L., Devos A., Suykens J.A.K., Vanhamme L., Howe F.A., Majos C., Moreno-Torres A., Van der Graaf M., Tate A.R., Arus C., Van Huffel S., "Brain Tumour Classification based on long echo proton MRS signals", Artificial Intelligence in Medicine, vol. 31, 2004, pp. 73-89.


220. Baesens B., Van Gestel T., Viaene S., Stepanove M., Suykens J.A.K., Vanthienen J., "Benchmarking state of the art classification algorithms for credit scoring", Journal of the Operational Research Society, vol. 54, no. 6, Jun. 2003, pp. 627-635.


221. Govoreanu B., Suykens J.A.K., Schoenmaker W., Dima G., Vandewalle J., Profirescu M., "On the use of Bayesian Learning Neural Networks for TCAD Empirical modeling", Romanian Journal of Information Science and Technology, vol. 5, no. 4, 2002, pp. 329-338.


222. Teughels A., De Roeck G., Suykens J.A.K., "Global optimization by coupled local minimizers and its applications to FE model updating", Computers and Structures, vol. 81, no. 24-25, sep. 2003, pp. 2337-2351.


223. Van Gestel T., Baesens B., Suykens J.A.K., Van den Poel D., Baestaens D.E., Willekens M., "Bayesian kernel based classification for financial distress detection", European Journal of Operational Research, vol. 172, no. 3, Aug. 2006, pp. 979-1003.


224. Goethals I., Van Gestel T., Suykens J.A.K., Van Dooren P., De Moor B., "Identification of Positive Real Models in Subspace Identification by using Regularization", IEEE Transactions on Automatic Control, vol. 48, no. 10, Oct. 2003, pp. 1843-1847.


225. Suykens J.A.K., Van Gestel T., Vandewalle J., De Moor B., "A support vector machine formulation to PCA analysis and its kernel version", IEEE Transactions on Neural Networks, vol. 14, no. 2, Mar. 2003, pp. 447-450.


226. Lu C., Van Gestel T., Suykens J.A.K., Van Huffel S., Vergote I., Timmerman D., "Preoperative prediction of malignancy of ovarium tumor using least squares support vector machines", Artificial Intelligence in Medicine, vol. 28, no. 3, Jul. 2003, pp. 281-306.


227. Suykens J.A.K., "Support Vector Machines : a nonlinear modelling and control perspective", European Journal of Control, Special Issue on fundamental issues in control, vol. 7, no. 2-3, Aug. 2001, pp. 311-327.


228. Duhoux M., Suykens J.A.K., De Moor B., Vandewalle J., "Improved long-term temperature prediction by chaining of neural networks", International Journal of Neural Systems, vol. 11, no. 1, Jan. 2001, 10 p.


229. Viaene S., Baesens B., Van Gestel T., Suykens J.A.K., Van den Poel D., Dedene D., De Moor B., Vanthienen J., "Knowledge discovery in a direct marketing case using least squares support vector machines", International Journal of Intelligent Systems, Vol. 16, no. 9, 2001, pp. 1023-1036.


230. Yalcin M., Ozoguz S., Suykens J.A.K., Vandewalle J., "Families of Scroll Grid Attractors", International Journal of Bifurcation and Chaos, vol. 12, no. 1, Jan. 2002, pp. 23-41.


231. Suykens J.A.K., Vandewalle J., De Moor B., "Intelligence and Cooperative Search by Coupled Local Minimizers", International Journal of Bifurcation and Chaos, vol. 11, no. 8, aug. 2001, pp. 2133-2144.


232. Yalcin M., Ozoguz S., Suykens J.A.K., Vandewalle J., "n-scroll chaos generators : a simple circuit model", Electronics Letters, vol. 37, no. 3, Feb. 2001, pp. 147-148.


233. Suykens J.A.K., De Brabanter J., Lukas L., Vandewalle J., "Weighted least squares support vector machines : robustness and sparse approximation", Neurocomputing, Special issue on fundamental and information processing aspects of neurocomputing, vol. 48, no. 1-4, Oct. 2002, pp. 85-105.


234. Van Gestel T., Suykens J.A.K., Lanckriet G., Lambrechts A., De Moor B., Vandewalle J., "Multiclass LS-SVMs : Moderated outputs and coding-decoding schemes", Neural Processing Letters, vol. 15, no. 1, Feb. 2002, pp. 45-48.


235. Van Gestel T., Suykens J.A.K., Baestaens D., Lambrechts A., Lanckriet G., Vandaele B., De Moor B., Vandewalle J., "Financial Time Series Prediction using Least Squares Support Vector Machines within the Evidence Framework", IEEE Transactions on Neural Networks, Special Issue on Neural Networks in Financial Engineering, vol. 12, no. 4, Jul. 2001, pp. 809-821.


236. Yalcin M.E., Suykens J.A.K., Vandewalle J., "Master-Slave Synchronization of Lur'e systems with Time-Delay", International Journal of Bifurcation and Chaos, vol. 11, no. 6, Jun. 2001, pp. 1707-1722.


237. Van Gestel T., Suykens J.A.K., Lanckriet G., Lambrechts A., De Moor B., Vandewalle J., "Bayesian Framework for Least Squares Support Vector Machine Classifiers, Gaussian Processes and Kernel Fisher Discriminant Analysis", Neural Computation, vol. 15, no. 5, May 2002, pp. 1115-1148.


238. Van Gestel T., Suykens J.A.K., Baesens B., Viaene S., Vanthienen J., Dedene G., De Moor B., Vandewalle J., "Benchmarking Least Squares Support Vector Machine Classifiers", Machine Learning, vol. 54, no. 1, Jan. 2004, pp. 5-32.


239. Suykens J.A.K., "Least squares support vector machines for classification and nonlinear modelling", Neural Network World, Special Issue on PASE 2000, vol. 10, no. 1-2, Jan. 2000, pp. 29-48.


240. Van Gestel T., Suykens J.A.K., Van Dooren P., De Moor B., "Identification of stable models in subspace identification by using regularization", IEEE Transactions on Automatic Control, vol. 46, no. 9, Sep. 2001, pp. 1416-1420.


241. Van Gestel T., Suykens J.A.K., De Moor B., Baestaens D.-E., "Volatility Tube Support Vector Machines", Neural Network World, Special Issue on PASE 2000, vol. 10, no. 1-2, Jan. 2000, pp. 287-297.


242. Suykens J.A.K., Vandewalle J., "Chaos Synchronization : a Lagrange Programming Network Approach", International Journal of Bifurcation and Chaos, Special Issue on Chaos Control and Synchronization, vol. 10, no. 4, Apr. 2000, pp. 797-810.


243. Suykens J.A.K., Vandewalle J., "Recurrent least squares support vector machines", IEEE Transactions on Circuits and Systems-I, vol. 47, no. 7, Jul. 2000, pp. 1109-1114.


244. Yalcin M.E., Suykens J.A.K., Vandewalle J., "Experimental confirmation of 3- and 5-scroll attractors from a generalized Chua's circuit", IEEE Transactions on Circuits and Systems, Part I : Fundamental Theory and Applications, vol. 47, no. 3, Mar. 2000, pp. 425-429.


245. Suykens J.A.K., Vandewalle J., "Chaos control using least squares support vector machines", International Journal of Circuit Theory and Applications, Special Issue on Communications, Information Processing and Control using Chaos, vol. 27, no. 6, Nov. 1999, pp. 605-615.


246. Suykens J.A.K., Vandewalle J., De Moor B., "Optimal control by least squares support vector machines", Neural Networks, vol. 14, no. 1, Jan. 2001, pp. 23-35.


247. McNames J., Suykens J.A.K., Vandewalle J., "Winning Entry of the K.U.Leuven Time-Series Prediction Competition", International Journal of Bifurcation and Chaos, vol. 9, no. 8, Aug. 1999, pp. 1485-1500.


248. Suykens J.A.K., Vandewalle J., "Least squares support vector machine classifiers", Neural Processing Letters, vol. 9, no. 3, Jun. 1999, pp. 293-300.


249. Suykens J.A.K., Vandewalle J., "Training multilayer perceptron classifiers based on a modified support vector method", IEEE Transactions on Neural Networks, vol. 10, no. 4, Jul. 1999, pp. 907-911.


250. Yang T., Suykens J.A.K., Chua L.O., "Impulsive control of nonautonomous chaotic systems using practical stabilization", International Journal of Bifurcation and Chaos, vol. 8, no. 7, Jul. 1998, pp. 1557-1564.


251. Suykens J.A.K., Yang T., Chua L.O., "Impulsive synchronization of chaotic Lur'e systems by measurement feedback", International Journal of bifurcation and Chaos, vol. 8, no. 6, Jun. 1998, pp. 1371-1381.


252. Suykens J.A.K., De Moor B.L.R., Vandewalle J., "Robust local stability of multilayer recurrent neural networks", IEEE Transactions on Neural Networks, vol. 11, no. 1, Jan. 2000, pp. 222-229.


253. Munuzuri A.P., Suykens J.A.K., Chua L.O., "A CNN approach to brain-like chaos-periodicity transitions", International Journal of Bifurcation and Chaos, vol. 8, no. 11, Nov. 1998, pp. 2263-2278.


254. Suykens J.A.K., Verrelst H., Vandewalle J., "On-line learning Fokker-Planck Machine", Neural Processing Letters, vol. 7, no. 2, Apr. 1998, pp. 81-89.


255. Suykens J.A.K., Curran P.F., Vandewalle J., Chua L.O., "Robust nonlinear H$_{\infty}$ synchronization of chaotic Lur'e systems", IEEE Transactions on Circuits and Systems I : Fundamental Theory and Applications, Special Issue on Chaos Synchronization and Control : Theory and Applications, vol. 44, no. 10, Oct. 1997, pp. 891-904.


256. Verrelst H., Van Acker K., Suykens J.A.K., Motmans B., De Moor B., Vandewalle J., "Application of NL$_{q}$ neural control theory to a ball and beam system", European Journal of control, vol. 4, no. 3, Sep. 1998, pp. 148-157.


257. Suykens J.A.K., Curran P.F., Chua L.O., "Robust synthesis for master-slave synchronization of Lur'e systems", IEEE Transactions on Circuits and Systems-I, vol. 46, no. 7, Jul. 1999, pp. 841-850.


258. Suykens J.A.K., Huang A., Chua L.O., "A family of n-scroll attractors from a generalized Chua's circuit", Archive fur Elektronik und Ubertragungstechnik (International Journal of Electronics and Communications), Special Issue at the occasion of Prof. Lueder's 65th birthday, vol. 51, no. 3, May 1997, pp. 131-138.


259. Suykens J.A.K., Curran P.F., Yang T., Vandewalle J., Chua L.O., "Nonlinear H$_{\infty}$ synchronization of Lur'e systems : dynamic output feedback case", IEEE Transactions on Circuits and Systems-I, Fundamental Theory and Applications, vol. 44, no. 11, Nov. 1997, pp. 1089-1092.


260. Curran P.F., Suykens J.A.K., Chua L.O., "Absolute stability theory and master-slave synchronization", International Journal Bifurcation and Chaos, vol. 7, no. 12, Dec. 1997, pp. 2891-2896.


261. Suykens J.A.K., Curran P.F., Chua L.O., "Master-slave synchronization using dynamic output feedback", International Journal of Bifurcation and Chaos, vol. 7, no. 3, Mar. 1997, pp. 671-679.


262. Suykens J.A.K., Vandewalle J., Chua L.O., "Nonlinear H$_{\infty}$ synchronization of chaotic Lur'e systems", International Journal of Bifurcation and Chaos, vol. 7, No. 6, Jun. 1997, pp. 1323-1335.


263. Suykens J.A.K., Chua L.O., "n-Double scroll hypercubes in 1D-CNNs", International Journal of Bifurcation and Chaos, vol. 7, no.8, 1997, pp. 1873-1885.


264. Suykens J.A.K., Vandewalle J., De Moor B., "NL$_{q}$ theory: checking and imposing stability of recurrent neural networks for nonlinear modeling", IEEE Transactions on Signal Processing, vol. 45, no. 11, Nov. 1997, pp. 2682-2691.


265. Suykens J.A.K., Lemmerling P., Favoreel W., De Moor B., Crepel M., Briol P., "Modeling the Belgian gas consumption using neural networks", Neural Processing Letters, Vol. 4, no. 3, Dec. 1996, pp. 157-166.


266. Suykens J.A.K., Vandewalle J., De Moor B., "Lur'e systems with multilayer perceptron and recurrent neural networks: absolute stability and dissipativity", IEEE Transactions on Automatic Control, vol. 44, no. 4, Apr. 1999, pp. 770-774.


267. Suykens J.A.K., Vandewalle J., "Master-slave synchronization of Lur'e systems", International Journal of Bifurcation and Chaos, Vol. 7, no. 3, Mar. 1997, pp. 665-669.


268. Suykens J.A.K., Vandewalle J., De Moor B., "An absolute stability criterion for the Lur'e problem with sector and slope restricted nonlinearities", IEEE Transactions on Circuits and Systems - I : Fundamental Theory and Applications, Vol. 45, no. 9, Sep. 1998, pp. 1007-1009.


269. Suykens J.A.K., Vandewalle J., "Control of a recurrent neural network emulator for the double scroll", IEEE Transactions on Circuits and Systems-I, vol. 43, no. 6, Jun. 1996, pp. 511-514.


270. Suykens J.A.K., Vandewalle J., "Learning a simple recurrent neural state space model to behave like Chua's double scroll", IEEE Transactions on Circuits and Systems-I, vol. 42, no. 8, Aug. 1995, pp. 499-502.


271. Suykens J.A.K., Vandewalle J., Van Ginderdeuren J., "Feedback linearization of nonlinear distortion in electrodynamic loudspeakers", Journal of the Audio Engineering Society, vol. 43, no. 9, Sep. 1995, pp. 690-694.


272. Suykens J.A.K., Vandewalle J., "Discrete time interconnected cellular neural networks within NL$_{q}$ theory", International Journal of Circuit Theory and Applications, vol. 24, no. 1, Jan.-Feb. 1996, pp. 25-36.


273. Suykens J.A.K., De Moor B., Vandewalle J., "NL$_{q}$ theory: a neural control framework with global asymptotic stability criteria", Neural Networks, vol. 10, no. 4, Jun. 1997, pp. 615-637.


274. Suykens J.A.K., De Moor B., Vandewalle J., "Nonlinear system identification using neural state space models, applicable to robust control design", International Journal of Control, vol. 62, no. 1, 1995, pp. 129-152.


275. Suykens J.A.K., Vandewalle J., "Generation of n-double scrolls (n=1,2,3,4,...)", IEEE Transactions on Circuits and Systems-I, Special issue on chaos in nonlinear electronic circuits, vol. 40, no. 11, 1993, pp. 861-867.


276. Suykens J.A.K., De Moor B., Vandewalle J., "Static and dynamic stabilizing neural controllers applicable to transition between equilibrium points", Neural Networks, vol. 7, no. 5, 1994, pp. 819-831.


277. Suykens J.A.K., Vandewalle J., "Quasilinear approach to nonlinear systems and the design of n-double scroll (n=1,2,3,4,...)", IEE Proceedings-G, vol. 138, no. 5, Oct. 1991, pp. 595-603.

National Journal Papers

1. Suykens J.A.K., "Gedrag in complexe netwerken", Karakter, no. 20, 2007, pp. 26-27.


2. Suykens J.A.K., Bersini H., "Neural control theory: an overview", Journal A, vol. 37, no. 3, 1996, pp. 4-10.

International Conference Papers

1. De Plaen H., Suykens J.A.K., "A Dual Formulation for Probabilistic Principal Component Analysis", in ICML 2023 Workshop on Duality Principles for Modern Machine Learning (DP4ML), Honolulu, Hawai, Jul. 2023, pp. 1-13.


2. Tonin F., Patrinos P., Suykens J.A.K., "Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers", in Proc. of the ECML PKDD 2023 - Workshop on Simplification, Compression, Efficiency and Frugality for Artificial intelligence, Torino, Italy, Sep. 2023, pp. 5.


3. Tonin F., Lambert A., Patrinos P., Suykens J.A.K., "Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms", in Proc. of the 40th International Conference on Machine Learning (ICML), Hawai, USA, Jul. 2023, 34379 p.


4. Achten S., Tonin F., Patrinos P., Suykens J. A. K., "Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification", in Proc. of the AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada, Mar. 2024, pp. 10766 - 10774.


5. Liu J., Tao Q., Zhu C., Liu Y., Suykens J.A.K., "Tensorized LSSVMs for Multitask Regression", in Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Rhodes Island, Greece, Jun. 2023, pp. 1-5.


6. Orchel M., Suykens J.A.K., "Improved update rule and sampling of stochastic gradient descent with extreme early stopping for support vector machines", in Proc. of the 7th International Conference on Machine Learning, Optimization, and Data Science (LOD), 1st symposium on Artificial Intelligence and Neuroscience (ACAIN), Electr, Network, Oct. 2021 Machine Learning, Optimization, and Data Science, Springer, pp. 147-161.


7. Pandey A., De Meulemeester H., De Plaen H., De Moor B., Suykens J.A.K., "Recurrent Restricted Kernel Machines for Time-series Forecasting", in Proc. of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2022) (ESANN 2022), Brugge, Belgium, Oct. 2022.


8. De Cooman B., Suykens J., Ortseifen A., "Enforcing hard state-dependent action bounds on deep reinforcement learning policies", in The 8th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science (LOD 2022), Siena, Italy, Sep. 2022, pp. 193-218.


9. Evens B., Latafat P., Themelis A., Suykens J., Patrinos P., "Neural Network Training as an Optimal Control Problem: An Augmented Lagrangian Approach", in Proc. of the 2021 60th IEEE Conferene on Decision and Control (CDC), Austin, USA, Dec. 2021, pp. 5136-5143.


10. Liu F., Suykens J.A.K., Cevher V., "On the Double Descent of Random Features Models Trained with SGD", in Advances in Neural Information Processing Systems (Neurips 2022), vol. 35, New Orleans, USA, 2022, pp. 34966-34980.


11. Chen Y., Shen X., Hu S.X., Suykens J.A.K, "Boosting Co-teaching with Compression Regularization for Label Noise", in CVPR Learning from Limited and Imperfect Data Workshop (L2ID@CVPR2021), New York, Jun. 2021.


12. Schreurs J., De Meulemeester H., Fanuel M., De Moor B., Suykens J.A.K, "Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks", in Proc. of the conference on machine Learning, Optimization and Data science (LOD), Grasmere, England, Oct. 2021, 466–480 p.


13. Meulemans A., Carzaniga F., Suykens J.A.K., Sacramento J., Grewe B.F., "A theoretical framework for target propagation", in Advances in Neural Information Processing Systems 33 (NeurIPS 2020), - , virtual, Dec. 2020.


14. Orchel M., Suykens J.A.K., "Fast Hyperparameter Tuning for Support Vector Machines with Stochastic Gradient Descent", in Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science, vol 12566. Springer, (LOD 2020), Siena, Italy, Jul. 2020, pp. 481-493.


15. Tonin F., Pandey A., Patrinos P., Suykens J. A.K., "Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine", in Proc. of the 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China, 2021, pp. 1-8.


16. Liu F., Liao Z., Suykens J.A.K., "Kernel regression in high dimension: Refined analysis beyond double descent", in Proc. of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS-2021), California, USA, Apr. 2021, pp. 1-11.


17. Schreurs J., Fanuel M., Suykens J.A.K, "Ensemble Kernel Methods, Implicit Regularization and Determinantal Point Processes", in ICML 2020 workshop on Negative Dependence and Submodularity for ML, online conf, Jul 2020, 6 p.


18. De Meulemeester H., Schreurs J., Fanuel M., De Moor B., Suykens J.A.K., "The Bures Metric for Generative Adversarial Networks", in Machine Learning and Knowledge Discovery in Databases., (Oliver N., Pérez-Cruz F., Kramer S., Read J., and Lozano J.A., eds.), Research Track. ECML PKDD 2021, vol. 12976 of mph{Lecture Notes in Computer Science}, Springer, Cham., 2021, pp. 52-66.


19. Liu F., Huang X., Chen Y., Suykens J.A.K., "Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures", in Proc. of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS-2021), California, USA, Apr. 2021, pp. 1-11.


20. De Plaen H., Fanuel M., Suykens J. A. K., "Wasserstein Exponential Kernels", in Proceedings of 2020 International Joint Conference on Neural Networks (IJCC), Glasgow, UK, Jul. 2020, 6 p.


21. Pandey A., Schreurs J., Suykens J. A. K., "Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality", in Lecture Notes in Computer Science, (Nicosia G., Ojha V., La Malfa E., Jansen G., Sciacca V., Pardalos P., Giuffrida G., and Umeton R., eds.), Proc. of the Machine Learning, Optimization, and Data Science, LOD 2020, Siena, Italy., vol. 12565 of LNCS, pp. 613-624.


22. Kazmi H., Suykens J., Driesen J., "Large-scale transfer learning for data-driven modelling of hot water systems", in Proc. of the International Building Performance Simulation Association (Building Simulation 2019), Rome, Italy, Sep. 2019, 8 p.


23. Liu F., Huang X., Chen Y., Yang J., Suykens J.A.K., "Random Fourier Features via Fast Surrogate Leverage Weighted Sampling", in Proc. of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2020), New York, Feb. 2020, pp. 4844-4851.


24. Winant D., Schreurs J., Suykens J.A.K., "Latent Space Exploration Using Generative Kernel PCA", in Proceedings of Bogaerts B. et al. (eds) Artificial Intelligence and Machine Learning. BNAIC 2019, BENELEARN 2019. Communications in Computer and Information Science, vol 1196. Springer, Cham. (BNAIC 2019, BENELEARN 2019), Brussels, Belgium, Nov. 2019, pp. 70-82.


25. Schreurs J., Fanuel M., Suykens J.A.K, "Towards deterministic diverse subset sampling", in Proc. of Bogaerts B. et al. (eds) Artificial Intelligence and Machine Learning. BNAIC 2019, BENELEARN 2019. Communications in Computer and Information Science, vol 1196. Springer, Cham. (BNAIC 2019, Benelearn 2019), Brussels, Belgium, Nov. 2019, pp. 137-151.


26. Tao Q., Xu J., Suykens J.A.K., Wang S., "Fast Adaptive Hinging Hyperplanes", in Proc. of the 2018 IEEE Conference on Decision and Control (CDC 2018), Miami Beach FL, USA, Dec. 2018, pp. 1482-1487.


27. Mehrkanoon S., Blaschko M.B., Suykens J.A.K., "Shallow and Deep Models for Domain Adaptation problems", in Proc. of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), Brugge, Belgium, Apr. 2018, pp. 291-299.


28. Kazmi H., Suykens J.A.K., Driesen J., "Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings", in Proc. of the17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018), Stockholm, Sweden, Jul. 2018, pp. 585-587.


29. Houthuys L., Suykens J.A.K., "Tensor Learning in Multi-View Kernel PCA", in Proc. of The 27th International Conference on Artificial Neural Networks (ICANN), Rhodes, Greece, Oct. 2018, pp. 205-215.


30. Karevan Z., Houthuys L., Suykens J. A. K., "Weighted Multi-view Deep Neural Networks for Weather Forecasting", in Proc. of the International Conference on Artificial Neural Networks 2018 (ICANN 2018), Rhodes, Greece, Oct. 2018, pp. 489-499.


31. Schreurs J., Suykens J.A.K., "Generative Kernel PCA", in Proc. of the European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, Apr. 2018, pp. 129-134.


32. Salzo S., Suykens J.A.K., Rosasco L., "Solving lp-norm regularization with tensor kernels", in Proc. of thethe Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS 2018), Lanzarote, Canary Islands, Apr. 2018, pp. 1655-1663.


33. Singaravel S., Geyer P., Suykens J.A.K., "Deep Neural Network Architectures for Component-Based Machine Learning Model in Building Energy Predictions", in Proc. of the European Group for Intelligent Computing in Engineering (EG-ICE), Nottingham, UK, Jul. 2017, pp. 260-268.


34. Houthuys L., Suykens J.A.K., "Unpaired Multi-View Kernel Spectral Clustering", in Proc. of the IEEE Symposium Series on Computational Intelligence (SSCI 2017), Hawaii, USA, Nov. 2017, pp. 1307-1313.


35. Sopasakis P., Themelis A., Suykens J., Patrinos P., "A primal-dual line search method and applications in image processing", in 2017 25th European Signal Processing Conference (EUSIPCO), Kos, Greece, Aug. 2017, pp. 1065-1069.


36. Bottegal G., Castro-Garcia R., Suykens J.A.K., "On the identification of Wiener systems with polynomial nonlinearity", in Proc. of the2017 IEEE 56th Annual Conference on Decision and Control (CDC 2017), Melbourne, Australia, Dec. 2017, pp. 6475-6480.


37. Castro-Garcia R., Agudelo M., Suykens J.A.K, "MIMO Hammerstein System Identification using LS-SVM and Steady State Time Response", in Proc. of the IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), Hawaii, USA, Nov. 2017, pp. 2559-2565.


38. Castro-Garcia R., Agudelo M., Suykens J.A.K., "Impulse Response Constrained LS-SVM modeling for Hammerstein System Identification", in Proc. of the 20th World Congress of the International Federation of Automatic Control (IFAC 2017), Toulouse, France , Jul. 2017.


39. Mehrkanoon S., Zell A., Suykens J.A.K., "Scalable Hybrid Deep Neural Kernel Networks", in Proc. of the 25th European Symposium on Artificial Neural Networks (ESANN) 2017, Special Session: Deep and kernel methods, Bruges, Belgium, Apr. 2017, pp. 17-22.


40. Karevan Z., Feng Y., Suykens J.A.K., "Moving Least Squares Support Vector Machines for weather temperature prediction", in Proc. of the European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, Apr. 2017, 6 p.


41. Bottegal G., Suykens J.A.K., "Probabilistic Matrix Factorization from Quantized Measurements", in Proc. of the 2017 International Joint Conference on Neural Networks (IJCNN 2017), Anchorage, US, May 2017, pp. 270-277.


42. Houthuys L., Karevan Z., Suykens J.A.K., "Multi-view LS-SVM Regression for Black-Box Temperature Prediction in Weather Forecasting", in Proc. of the International Joint Conference on Neural Networks (IJCNN), Anchorage, USA, May 2017, pp. 1102-1108.


43. Langone R., Suykens J. A. K., "Efficient multiple scale kernel classifiers", in Proc. of the International Conference on Big Data (IEEE Big Data), Washington D. C., USA, Dec. 2016, pp. 128-133.


44. Aliquintuy M., Frandi E., Nanculef R., Suykens J.A.K., "Efficient Sparse Approximation of Support Vector Machines Solving a Kernel Lasso", in CIARP 2016: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Lima, Peru, Nov. 2016, pp. 208-216.


45. Mehrkanoon S., Suykens J.A.K., "Scalable Semi-Supervised Kernel Spectral Learning using Random Fourier Features", in Proc. of the IEEE Symposium Series on Computational Intelligence ((SSCI-CIDM)), Athens, Greece, Dec. 2016, pp. 1-8.


46. Karevan Z., Suykens J.A.K., "Clustering-based feature selection for black-box weather temperature prediction", in Proc. of the International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, Jul. 2016, pp. 1-8.


47. Castro-Garcia R., Suykens J.A.K., "Wiener System Identification using Best Linear Approximation within the LS-SVM framework", in Proc. of the 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI), Cartagena, Colombia, Nov. 2016.


48. Mall R., Bensmail H., Langone R., Varon C., Suykens J., "Denoised Kernel Spectral Data Clustering", in Proc. of the International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, Jul. 2016, pp. 3709-3716.


49. Mehrkanoon S., Suykens J.A.K., "Multi-label semi-supervised learning using regularized Kernel spectral clustering", in Proc. of the IEEE World Congress on Computational Intelligence (IEEE WCCI/IJCNN 2016), Vancouver, Canada , Jul. 2016, pp. 4009-4016.


50. Karevan Z., Suykens J.A.K., "Spatio-temporal feature selection for black-box weather forecasting", in Proc. of the European Symposium on Artificial Neural Networks (ESANN ), Bruges, Belgium, Apr. 2016, pp. 611-616.


51. Langone R., Mall R., Jumutc V., Suykens J. A. K., "Fast in-memory spectral clustering using a fixed-size approach", in Proc. of the 24 European Symposium on Artificial Neural Networks, Computational Intelligence and machine Learning (ESANN 2016), Brugge, Belgium, Apr. 2016, pp. 557-562.


52. Castro-Garcia R., Agudelo M., Tiels K., Suykens J.A.K, "Hammerstein System Identification using LS-SVM and Steady State Time Response", in Proc. of the European Control Conference (ECC), Aalborg, Denmark, Jul. 2016.


53. Jumutc V., Langone R., Suykens J.A.K., "Regularized and Sparse Stochastic K-Means for Distributed Large-Scale Clustering", in Proc. of the IEEE International Conference on Big Data (IEEE Big Data 2015), Santa Clara, California (USA), Oct. 2015, pp. 2535-2540.


54. Chandorkar M., Mall R., Lauwers O., Suykens J. A. K., De Moor B., "Fixed-Size Least Squares Support Vector Machines: Scala Implementation for Large Scale Classification", in Proc. of the IEEE Symposium Series on Computational Intelligence (IEEE SSCI), Cape Town, South Africa, Dec. 2015.


55. Houthuys L., Langone R., Suykens J.A.K., "Clustering From Two Data Sources Using a Kernel-Based Approach with Weight Coupling", in Proc. of the European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, Apr. 2016, pp. 569-574.


56. Castro-Garcia R., Tiels K., Schoukens J., Suykens J.A.K., "Incorporating Best Linear Approximation within LS-SVM-Based Hammerstein System Identification", in Proc. of the 54th IEEE Conference on Decision and Control (CDC), Osaka, Japan, Dec. 2015, pp. 7392-7397.


57. Karevan Z., Mehrkanoon S., Suykens J.A.K., "Black-box modeling for temperature prediction in weather forecasting", in Proc. of the International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, Jul. 2015, pp. 1-8.


58. Mall R., Suykens J.A.K., "Kernel Spectral Document Clustering Using Unsupervised Precision-Recall Metrics", in Proc. of the 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, Jul. 2015, pp. 1-7.


59. Mehrkanoon S., Agudelo M., Mall R., Suykens J.A.K., "Hierarchical Semi-Supervised Clustering using KSC based model", in Proc. of the International Joint Conference on Neural Networks 2015 (IJCNN 2015), Killarney, Ireland , Jul. 2015, pp. 1-8.


60. Frandi E., Nanculef R., Suykens J. A. K., "A PARTAN-Accelerated Frank-Wolfe Algorithm for Large-Scale SVM Classification", in Proc. of the International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, Jul. 2015.


61. Frandi E., Nanculef R., Suykens J., "Complexity Issues and Randomization Strategies in Frank-Wolfe Algorithms for Machine Learning", in NIPS Workshop on Optimization for Machine Learning (OPT 2014), Montréal, Canada, Dec. 2014.


62. Moschopoulos C., Popovic D., Langone R., Suykens JAK., De Moor B., Moreau Y., "Gene interaction networks boost genetic algorithm performance in biomarker discovery", in Proc. of the IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, USA, Dec. 2014, pp. 144-149.


63. Mall R., Langone R., Suykens J.A.K., "Ranking Overlap and Outlier Points in Data using Soft Kernel Spectal Clustering", in Proc. of the European Symposium on Artificial Neural Networks (ESANN)}, Bruges, Belgium, Apr. 2015, 6 p.


64. Signoretto M., Frandi E., Karevan Z., Suykens J. A. K., "High Level High Performance Computing for Multitask Learning of Time-varying Models", in IEEE Symposium on Computational Intelligence in Big Data (IEEE CIBD 2014), Orlando, Florida (USA), Dec. 2014.


65. Mall R., Jumutc V., Langone R., Suykens J.A.K., "Representative Subsets For Big Data Learning using kNN graphs", in Proc. of the IEEE BigData (BigData), Washington DC, U.S.A., Oct. 2014, pp. 37-42.


66. Jumutc V., Suykens J.A.K., "New Bilinear Formulation to Semi-Supervised Classification Based on Kernel Spectral Clustering", in Proc. of the 2014 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014), Orlando, Florida, Dec. 2014, pp. 41-47.


67. Langone R., Alzate C., Bey-Temsamani A., Suykens J. A. K., "Alarm prediction in industrial machines using autoregressive LS-SVM models", in Proc. of the Symposium Series on Computational Intelligence (SSCI-CIDM), Orlando,Florida, Dec. 2014, pp. 359-364.


68. Mall R., Langone R., Suykens J.A.K., "Agglomerative Hierarchical Kernel Spectral Data Clustering", in Proc. of the IEEE Symposium on Computational Intelligence and Data Mining (SSCI CIDM), Orlando, U.S.A, Dec. 2014, pp. 9 - 16.


69. Jumutc V., Suykens J.A.K., "Reweighted l2-Regularized Dual Averaging Approach for Highly Sparse Stochastic Learning", in Proc. of the 11th International Symposium on Neural Networks (ISNN 2014), Hong-Kong & Makao, People's Republic of China , Nov. 2014, pp. 232-242.


70. Langone R., Mall R., Suykens J.A.K., "Clustering data over time using kernel spectral clustering with memory", in Proc. of the Symposium Series on Computational Intelligence (SSCI-CIDM), Orlando,Florida, Dec. 2014, pp. 1-8.


71. Castro-Garcia R., Mehrkanoon S., Marconato A., Schoukens J. and Suykens J. A. K., "SVD truncation schemes for fixed-size kernel models", in Proc. of the International Joint Conference on Neural Networks (IJCNN), Beijing, China, Jun. 2014, pp. 3922-3929.


72. Mehrkanoon S., Suykens J.A.K, "Large scale semi-supervised learning using KSC based model", in Proc. of the International Joint Conference on Neural Networks (IJCNN 2014) (IJCNN 2014), Beijing, China, Jul. 2014, pp. 4152-4159.


73. Signoretto M., Suykens J. A. K., "Identification of Structured Dynamical Systems in Tensor Product Reproducing Kernel Hilbert Spaces", in 21st International Symposium on Mathematical Theory of Networks and Systems (MTNS2014), Groningen, The Netherlands, Jul. 2014, pp. 1-5.


74. Vanderloock K., Vanden Abeele V., Suykens J.A.K., Geurts L., "The skweezee system: enabling the design and the programming of squeeze interactions", in Proc. of the26th annual ACM symposium on User interface software and technology, St Andrews, UK, Oct. 2013, pp. 521-530.


75. Jumutc V., Suykens J.A.K., "Reweighted l1 Dual Averaging Approach for Sparse Stochastic Learning", in Proc. of the 22th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2014), Bruges, Belgium, Apr. 2014, pp. 1 - 6.


76. Mall R., Langone R., Suykens J.A.K., "Agglomerative Hierarchical Kernel Spectral Clustering for Large Scale Networks", in Proc. of the European Symposium on Artitficial Neural Networks (ESANN), Brugges, Belgium, Apr. 2014.


77. Signoretto M., Suykens J. A. K., "Multilinear Spectral Regularization for Kernel-based Multitask Learning", in Proc. of the NIPS Workshop New Directions in Transfer and Multi-Task: Learning Across Domains and Tasks, Lake Tahoe, Nevada (US), Dec. 2013, 4 p.


78. Mall R., El Anbari M., Bensmail H., Suykens J.A.K., "Primal-Dual Framework for Feature Selection using Least Squares Support Vector Machines", in Proc. of the 19th International Conference on Management of Data (COMAD), Ahmedabad, India, Dec. 2013, pp. 105-108.


79. Mehrkanoon S., Quirynen R., Diehl M., Suykens J.A.K., "LSSVM based initialization approach for parameter estimation of dynamical systems", in Proc. of the International Conference on Mathematical Modelling in Physical Sciences (IC-MSQUARE 2013), Prague, Czech Republic, Sep. 2013, 4 p.


80. Mall R., Mehrkanoon S., Langone R., Suykens J.A.K., "Optimal Reduced Sets for Sparse Kernel Spectral Clustering", in Proc. of the International Joint Conference on Neural Networks (IJCNN), Beijing, China, Jul. 2014, pp. 2436 - 2443.


81. Jumutc V., Suykens J., "Weighted Coordinate-Wise Pegasos", in Proc. of the 5th International Conference on Pattern Recognition and Machine Intelligence (PREMI 2013), Kolkata, India, Dec. 2013, pp. 262-269.


82. Mall R., Langone R., Suykens J.A.K., "Highly Sparse Reductions to Kernel Spectral Clustering", in Proc. of the 5th International Conference on Pattern Recognition and Machine Intelligence (PReMI), Kolkata, India, Dec. 2013, pp. 163-169.


83. Mall R., Langone R., Suykens J.A.K., "Self-Tuned Kernel Spectral Clustering for Large Scale Networks", in Proc. of the IEEE International Conference on Big Data (IEEE BigData 2013), Santa Clara, United States of America, Oct. 2013.


84. Falck T., De Moor B., Suykens J.A.K., "Kernel based identification of systems with multiple outputs using nuclear norm regularization", in Book of Abstracts, KU Leuven (Leuven, Belgium), 2013.


85. Peluffo D., Garcia S., Langone R., Suykens J.A.K., Castellanos G., "Kernel Spectral Clustering for dynamic data using Multiple Kernel Learning", in Proc. of the (IJCNN 2013), Dallas, Texas, Aug. 2013, pp. 1085-1090.


86. Mehrkanoon S., Suykens J.A.K., "Non-parallel semi-supervised classification based on kernel spectral clustering", in Proc. of the International Joint Conference on Neural Networks (IJCNN 2013), Dallas, U.S.A, Aug. 2013, pp. 2311-2318.


87. Langone R., Mall R., Suykens J.A.K., "Soft Kernel Spectral Clustering", in Proc. of the (IJCNN 2013), Dallas, Texas, Aug. 2013, pp. 1028-1035.


88. Jumutc V., Huang X., Suykens J.A.K., "Fixed-Size Pegasos for Hinge and Pinball Loss SVM", in Proc. of the 2013 International Joint Conference on Neural Networks (IJCNN 2013), Dallas, USA, Aug. 2013, pp. 1122-1128.


89. Varon C., Testelmans D., Buyse B., Suykens J.A.K., Van Huffel S., "Sleep apnea classification using least-squares support vector machines on single lead ECG", in Proc. of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’13), Osaka, Japan, Jul. 2013, pp. 5029-5032.


90. Signoretto M., Suykens J. A. K., "DynOpt: Incorporating Dynamics into Mean-Variance Portfolio Optimization", in IEEE Symposium Series on Computational Intelligence 2013 (SSCI), Singapore, Signapore, Apr. 2013, 7 p.


91. Langone R., Alzate C., De Ketelaere B., Suykens J.A.K, "Kernel spectral clustering for predicting maintenance of industrial machines", in Proc. of the Symposium Series On Computational Intelligence (SSCI 2013), Singapore, Singapore, Apr. 2013, pp. 39-45.


92. Mall R., Suykens J.A.K., "Sparse Reductions for Fixed-Size Least Squares Support Vector Machines on Large Scale Data", in Proc. of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013), GoldCoast, Australia, Apr. 2013, pp. 161-173.


93. Langone R., Suykens J. A. K., "Community detection using Kernel Spectral Clustering with memory", in Proc. of the International Conference on Mathematical Modelling in Physical Sciences (IC-MSQUARE 2012), Budapest, Hungary, Sep. 2012.


94. Mehrkanoon S., Suykens J.A.K., "LS-SVM based solution for delay differential equations", in Proc. of the International Conference on Mathematical Modelling in Physical Sciences (IC-MSQUARE 2012), Budapest, Hungary , Sep. 2012, 4 p.


95. Jumutc V., Suykens J.A.K., "Supervised Novelty Detection", in Proc. of the IEEE Symposium Series on Computational Intelligence (SSCI 2013), Singapore, Singapore, Apr. 2013, pp. 143 - 149.


96. Varon C., Testelmans D., Buyse B., Suykens J.A.K, Van Huffel S., "Robust artefact detection in long-term ECG recordings based on autocorrelation function similarity and percentile analysis", in Proc. of the 34th Annual International Conference of the Engineering in Medicine and Biology Society (IEEE EMBC), San Diego, CA US, Aug. 2012, pp. 3151-3154.


97. Alzate C., Suykens J.A.K., "A Semi-Supervised Formulation to Binary Kernel Spectral Clustering", in Proc. of the 2012 IEEE World Congress on Computational Intelligence (IEEE WCCI/IJCNN 2012), Brisbane, Australia, Jun. 2012, pp. 1992-1999.


98. De Brabanter K., De Brabanter J., Suykens J.A.K., Vandewalle J., De Moor B., "Robustness of Kernel Based Regression: Influence and Weight Functions", in Proc. of the 2012 IEEE World Congress on Computational Intelligence (IJCNN), Brisbane, Australia, Jun. 2012, pp. 3387-3394.


99. Geebelen D., Batselier K., Dreesen P., Signoretto M., Suykens J.A.K., De Moor B., Vandewalle J., "Joint Regression and Linear Combination of Time Series for Optimal Prediction", in Proc. of the European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, Apr. 2012.


100. Van Belle V., Van Huffel S., Suykens J.A.K., Boyd S., "Interval coded scoring systems for survival analysis", in Proc. of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN2012), Brugge, Belgium, Apr. 2012, pp. 173-178.


101. Signoretto M., Suykens J.A.K., "Convex Estimation of Cointegrated VAR Models by a Nuclear Norm Penalty", in Proc. of the 16th IFAC Symposium on System Identification (Sysid), Bruxelles,Belgium, Jul. 2012.


102. Marconato A., Sjoberg J., Suykens J.A.K., Schoukens J., "Identification of the Silverbox Benchmark using Nonlinear State-Space Models", in Proc. of the 16th IFAC Symposium on System Identification (SYSID 2012), Brussels, Belgium, Jul. 2012, pp. 632-637.


103. Marconato A., Sjoberg J., Suykens J.A.K., Schoukens J., "Separate Initialization of Dynamics and Nonlinearities in Nonlinear State-Space Models", in Proc. of the 2012 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Graz, Austria, May 2012, pp. 2104 - 2108.


104. Mehrkanoon S., Falck T., Suykens J.A.K., "Parameter Estimation for Time Varying Dynamical Systems using Least Squares Support Vector Machines", in Proc. of the 16th IFAC Symposium on System Identification ( SYSID 2012), Brussels, Belgium, Jul. 2012, pp. 1300-1305.


105. Langone R., Alzate C. and Suykens J. A. K., "Kernel spectral clustering for community detection in complex networks", in Proc. of the 2012 IEEE World Congress on Computational Intelligence (IEEE WCCI/IJCNN 2012), Brisbane, Australia, Jun. 2012, pp. 2596-2603.


106. De Brabanter K., De Brabanter J., Gijbels I., Suykens J.A.K., De Moor B., "New Developments in Kernel Regression with Correlated Errors", in Graybill 2011 Conference on Modern Nonparametric Methods (Graybill), Fort Collins, Colorado, Jun. 2011.


107. Hunyadi B., De Vos M., Signoretto M., Suykens J.A.K., Van Paesschen W., Van Huffel S., "Automatic Seizure Detection Incorporating Structural Information", in Proc. of the 21st International Conference on Artificial Neural Networks, LNCS 6791 (ICANN 2011), Espoo, Finland, Jun. 2011, pp. 233-240.


108. Langone R., Alzate C., Suykens J.A.K., "Modularity-based model selection for kernel spectral clustering", in Proc. of the International Joint Conference on Neural Networks (IJCNN 2011), San Jose, U.S.A., Aug. 2011, pp. 1849-1856.


109. Alzate C., Suykens J.A.K., "Out-of-Sample Eigenvectors in Kernel Spectral Clustering", in Proc. of the International Joint Conference on Neural Networks (IJCNN 2011), San Jose, U.S.A., Aug. 2011, pp. 2349-2356.


110. Lou X., Suykens J.A.K., "Adaptive synchronizability of coupled oscillators with switching", in 24th Chinese Control and Decision Conference, CCDC 2012, vol. *, no. *, May 2012, pp. 330-334.


111. Mehrkanoon S., Jiang L., Alzate C., Suykens J.A.K., "Symbolic computing of LS-SVM based models", in Proc. of the 19th European Symposium on Artificial Neural Networks (ESANN 2011), Bruges, Belgium, Apr. 2011, pp. 183-188.


112. Lopez J., De Brabanter K., Dorronsoro J.R., Suykens J.A.K, "Sparse LS-SVMs with L0-Norm Minimization", in Proc. of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, Apr. 2011, pp. 189-194.


113. De Brabanter K., Karsmakers P., De Brabanter J., Pelckmans K., Suykens J.A.K., De Moor B., "On Robustness in Kernel Based Regression", in NIPS 2010 Workshop Robust Statistical Learning (ROBUSTML) (NIPS 2010), Whistler, Canada, Dec. 2010.


114. Signoretto M., De Lathauwer L., Suykens J.A.K., "Convex Multilinear Estimation and Operatorial Representations", in Proc. of the NIPS Workshop on Tensors, Kernels and Machine Learning 2010 (TKML), Whistler, Canada, Dec. 2010.


115. Falck T., Ohlsson H., Ljung L., Suykens J.A.K., De Moor B., "Segmentation of time series from nonlinear dynamical systems", in Proc. of the 18th World Congress of the International Federation of Automatic Control, Milan, Italy, Aug. 2011, pp. 13209-13214.


116. Ojeda F., Signoretto M., Van de Plas R., Waelkens E., De Moor B., Suykens J.A.K., "Semi-supervised Learning of Sparse Linear Models in Mass Spectral Imaging", in Pattern Recongintion in Bioinformatics, (Dijkstra T.M.H, Tsivtsivadze E., Marchiori E., and Heskes T., eds.), 5th IAPR International Conference, PRIB 2010 Nijmegen, The Netherlands, September 2010, vol. 6282 of Lecture Notes in Bioinformatics. Subseries of Lecture Notes in Computer Science, Springer-Verlag, 2010, pp. 325-334.


117. Signoretto M., De Lathauwer L., Suykens J.A.K., "Kernel-based Learning from Infinite Dimensional 2-way Tensors", in ICANN 2010, part II, LNCS 6353, (Diamantaras K., Duch W., and Iliadis L.S. , eds.), Springer-Verlag, 2010, pp. 59-69.


118. Combaz A., Chumerin N., Manyakov N.V., Suykens J.A.K., Van Hulle M.M., "Error-related potential recorded by EEG in the context of a P300 mind speller brain-computer interface", in Proc. of the 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), Kittila, Finland, Aug. 2010, pp. 65-70.


119. Ojeda F., Falck T., De Moor B., Suykens J.A.K., "Polynomial componentwise LS-SVM: fast variable selection using low rank updates", in Proc. of the International Joint Conference on Neural Networks (IJCNN 2010), Barcelona, Spain, Jul. 2010, pp. 3291-3297.


120. Falck T., Suykens J.A.K., Schoukens J., De Moor B., "Nuclear Norm Regularization for Overparametrized Hammerstein Systems", in Proc. of the 49th IEEE Conference on Decision and Control (CDC2010), Atlanta, GA, Dec. 2010, pp. 7202-7207.


121. Necoara I., Dumitrache I., Suykens J.A.K., "Fast primal-dual projected linear iterations for distributed consensus in constrained convex optimization", in Proc. of the 49th IEEE Conference on Decision and Control (CDC 2010), Atlanta, US, Dec. 2010, pp. 1366-1371.


122. Pelckmans K., van Waterschoot T., Suykens J.A.K., "Efficient Adaptive Filtering for Smooth Linear FIR Models", in Proc. of the 18th European Signal Process. Conf. (EUSIPCO 2010), Aalborg, Danmark, Aug. 2010, pp. 2136-2140.


123. Rogge J., Suykens J.A.K., Aeyels D., "Consensus over ring networks as a quadratic optimal control problem", in Proc. of the 4th IFAC Symposium on System, Structure and Control, Ancona, Italy, Sep. 2010, pp. 317 -323.


124. Falck T., Suykens J.A.K., De Moor B., "Linear Parametric Noise Models for Least Squares Support Vector Machines", in Proc. of the 49th IEEE Conference on Decision and Control (CDC2010), Atlanta, GA, Dec. 2010, pp. 6389-6394.


125. Signoretto M., Pelckmans K., De Lathauwer L., Suykens J.A.K., "Improved Non-Parametric Sparse Recovery with Data Matched Penalties", in 2nd International Workshop on Cognitive Information Processing (CIP), Elba Island, Italy, Apr. 2010, 6 p.


126. Alzate C., Suykens J.A.K., "Highly Sparse Kernel Spectral Clustering with Predictive Out-of-Sample Extensions", in Proc. of the 18th European Symposium on Artificial Neural Networks (ESANN 2010), Bruges, Belgium, Apr. 2010, pp. 235-240.


127. Signoretto M., Daemen A., Savorgnan C., Suykens J. A. K., "Variable Selection and Grouping with Multiple Graph Priors", in Proc. of the 2nd NIPS Workshop on Optimization for Machine Learning, Whistler, Canada , Dec. 2009.


128. Van Belle V., Pelckmans K., Suykens J.A.K., Van Huffel S., "On the use of a clinical kernel in survival analysis", in Proc. of the European Symposium on Artificial Neural Networks (esann2010) (ESANN2010)}, Bruges, Belgium, Apr. 2010, pp. 451-456.


129. De Brabanter K., Sahhaf S., Karsmakers P., De Brabanter J., Suykens J.A.K., De Moor B., "Nonparametric Comparison of Densities Based on Statistical Bootstrap", in Proc. of the Fourth European Conference on the Use of Modern Information and Communication Technologies (ECUMICT), Gent, Belgium, Mar. 2010, pp. 179-190.


130. De Brabanter K., De Brabanter J., Suykens J.A.K., De Moor B., "Kernel Regression with Correlated Errors", in Proc. of the the 11th International Symposium on Computer Applications in Biotechnology (CAB), Leuven, Belgium, Jul. 2010, pp. 13-18.


131. Combaz A., Manyakov N.V., Chumerin N., Suykens J.A.K., Van Hulle M.M., "Feature Extraction and Classification of EEG Signals for Rapid P300 Mind Spelling", in Proc. of the 2009 International Conference on Machine Learning and Applications (ICMLA 2009), Miami Beach Florida, US, Dec. 2009, pp. 386-391.


132. Petrov V.S., Osipov G.V., Suykens J.A.K., "Wave dynamics of oscillatory ensembles under the influence of passive elements", in Proc. of the 17th International Workshop on Nonlinear Dynamics of Electronic Systems (NDES 2009), Rapperswil, Switzerland, Jun. 2009, pp. 82-85.


133. Osipov G.V., Komarov M.A., Suykens J.A.K., "Transient dynamics in the network of Hodgkin-Huxley neurons", in Proc. of the 17th International Workshop on Nonlinear Dynamics of Electronic Systems (NDES 2009), Rapperswil, Switzerland, Jun. 2009, pp. 161-164.


134. Komarov M.A., Osipov G.V., Suykens J.A.K., Rabinovich M.I., "Emergence of slow rhythms in neural microcircuit: bifurcations and stability", in Proc. of the 17th International Workshop on Nonlinear Dynamics of Electronic Systems (NDES 2009), Rapperswil, Switzerland, Jun. 2009, pp. 168-171.


135. Chumerin N., Manyakov N.V., Combaz A., Suykens J.A.K., Yazicioglu R.F., Torfs T., Merken P., Neves H.P., Van Hoof C., Van Hulle M.M., "P300 detection based on Feature Extraction in on-line Brain-Computer Interface", in Proc. of the 32nd Annual Conference on Artificial Intelligence (KI 2009), Paderborn, Germany, Sep. 2009, pp. 339-346.


136. Chumerin N., Manyakov N.V., Combaz A., Suykens J.A.K., Van Hulle M.M., "An application of feature selection to on-line P300 detection in brain-computer interface", in Proc. of the 2009 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2009), Grenoble, France, Sep. 2009, pp. 1-6.


137. Suykens J.A.K., "Support vector machines and kernel-based learning for dynamical systems modelling", in Proc. of the 15th IFAC Symposium on System Identification (SYSID 2009), Saint-Malo, France, Jul. 2009, pp. 1029-1037.


138. Luts J., Laudadio T., Martinez-Bisbal M.C., Van Cauter S., Molla E., Piquer J., Suykens J.A.K., Himmelreich U., Celda B., Van Huffel S., "Differentiation between brain metastases and glioblastoma multiforme based on MRI, MRS and MRSI", in Proc. of the 22nd IEEE International Symposium on Computer-Based Medical Systems (CBMS), Albuquerque, New Mexico, Aug. 2009, pp. 1-8.


139. Pelckmans K., Suykens J.A.K., "Gossip Algorithms for Computing U-statistics", in Proc. of the 1st IFAC Workshop on Estimation and Control of Networked Systems (NecSys 2009), Venice, Italy, Sep. 2009.


140. Alzate C., Espinoza M., De Moor B., Suykens J.A.K., "Identifying Customer Profiles in Power Load Time Series Using Spectral Clustering", in Proc. of the 19th International Conference on Artificial Neural Networks (ICANN 2009), Limassol, Cyprus, Sep. 2009, pp. 315-324.


141. Falck T., Suykens J.A.K., De Moor B., "Robustness Analysis for Least Squares Kernel Based Regression: an Optimization Approach", in Proc. of the 48th IEEE Conference on Decision and Control (CDC 2009), Shanghai, China, Dec. 2009, pp. 6774-6779.


142. Necoara I., Suykens J.A.K., "An Interior Point Lagrangian Decomposition Method for Convex Programming", in 20th Meeting of the International Symposium on Mathematical Programming ISMP 2009 Chicago US, Aug. 2009, 20 p.


143. Necoara I., Suykens J.A.K., "A dual interior-point distributed algorithm for large-scale data networks optimization", in Proc. of the European Control Conference 2009 (ECC 2009), Budapest, Hungary, Aug. 2009, pp. .


144. Necoara I., Dumitrache I., Suykens J.A.K., "Smoothing Techniques for Distributed Model Predictive Algorithms in Networks", in Proc. of the 8th IFAC Workshop on Time Delay Systems (IFAC-TDS 2009), Sinaia, Romania, Sep. 2009, pp. .


145. Necoara I., Savorgnan C., Tran Dinh Q., Suykens J.A.K., Diehl M., "Distributed Nonlinear Optimal Control using Sequential Convex Programming and Smoothing Techniques", in 48th IEEE Conference on Decision and Control (CDC 2009),Shanghai China 2009, 6 p.


146. De Brabanter K., Pelckmans K., De Brabanter J., Debruyne M., Suykens J.A.K., Hubert M., De Moor B., "Robustness of Kernel Based Regression: a Comparison of Iterative Weighting Schemes", in Proc. of the 19th International Conference on Artificial Neural Networks (ICANN), Limassol, Cyprus, Sep. 2009, pp. 100-110.


147. Van Belle V., Pelckmans K., Suykens J.A.K., Van Huffel S., "MINLIP: Efficient Learning of Transformation Models", in Artificial Neural Networks - ICANN 2009, (Alippi C., Polycarpou M., Panayiotou C., and Ellinas G., eds.), Proceedings of the International Conference on Artificial Neural Networks (ICANN2009), vol. 5768 (part 1) of Lecture notes in Computer Science, Springer, 2009, pp. 60-69.


148. Tsiaflakis P., Necoara I., Suykens J.A.K., Moonen M., "An improved dual decomposition approach to DSL dynamic spectrum management", in Proc. of the 17th European Signal Processing Conference (EUSIPCO), Glasgow, Scotland, Aug. 2009, pp. 1-5.


149. Schoukens J., Suykens J.A.K., Ljung L., "Wiener-Hammerstein Benchmark", in Proc. of the 15th IFAC Symposium on System Identification (SYSID 2009), Saint-Malo, France, Jul. 2009, pp. 531-552.


150. Van Belle V., Pelckmans K., Suykens J.A.K., Van Huffel S., "Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation Constraints", in Bio-Inspired Systems: Computational and Ambient Intelligence, (Cabestany J., Sandoval F., Prieto A., and and Corchabo J.M., eds.), Proc. of the 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, vol. 5517 of Lecture notes in Computer Science, Springer, 2009, 2009, pp. 65-72.


151. Alzate C., Suykens J.A.K., "A Regularized Formulation for Spectral Clustering with Pairwise Constraints", in Proc. of the 2009 International Joint Conference on Neural Networks (IJCNN'09), Atlanta, U.S.A, Jun. 2009, pp. 141-148.


152. Pelckmans K., Suykens J.A.K., "LPRankBoost and Column Generation", in Proc. of the International Conference/Euro Mini Conference on Continuous Optimization and Knowledge-Based Technologies (EurOPT 2008) (EurOPT 2008), Neringa, Lithuania, Jun. 2008, pp. 165-169.


153. Pelckmans K., Suykens J.A.K., "Transductively Learning from Positive Examples Only", in Proc. of the European Symposium on Artificial Neural Networks (esann 2009), Bruges, Belgium, Mar. 2009, pp. 23-28.


154. Falck T., Pelckmans K., Suykens J.A.K., De Moor B., "Identification of Wiener-Hammerstein Systems using LS-SVMs", in Proceedings of the 15th IFAC Symposium on System Identification (SYSID 2009), Saint-Malo, France, Jul. 2009, pp. 820-825.


155. De Brabanter K., Dreesen P., Karsmakers P., Pelckmans K., De Brabanter J., Suykens J.A.K., De Moor B., "Fixed-Size LS-SVM Applied to the Wiener-Hammerstein Benchmark", in Proc. of the 15th IFAC Symposium on System Identification (SYSID 2009), Saint-Malo, France, Jul. 2009, pp. 826-831.


156. Verstraeten D., Xavier-de-Souza S., Schrauwen B., Suykens J.A.K., Stroobandt D., Vandewalle J., "Pattern classification with CNNs as reservoirs", in Proc. of theInternational Symposium on Nonlinear Theory and its Applications (NOLTA 2008), Budapest, Hungary, Sep. 2008, pp. 101-104.


157. Espinoza M., Falck T., Suykens J.A.K., De Moor B., "Time Series Prediction using LS-SVMs", in Proc. of the European Symposium on Time Series Prediction (ESTSP08), Porvoo, Finland, Sep. 2008, pp. 159-168.


158. Necoara I., Doan D., Suykens J.A.K., "Application of the proximal center decomposition method to distributed model predictive control", in Proc. of the IEEE Conference on Decision and Control (CDC 2008), Cancun, Mexic, Dec. 2008.


159. Signoretto M., Pelckmans K., Suykens J.A.K., "Quadratically Constrained Quadratic Programming for Subspace Selection in Kernel Regression Estimation", in Proc. of the 18th International Conference on Artificial Neural Networks (ICANN), Prague, Czech Republic, Sep. 2008.


160. Necoara I., Suykens J.A.K., "A proximal center-based decomposition method for multi-agent convex optimization", in Proc. of the IEEE Conference on Decision and Control (CDC 2008), Cancun, Mexic, Dec. 2008.


161. Alzate C., Suykens J. A. K., "Sparse Kernel Models for Spectral Clustering Using the Incomplete Cholesky Decomposition", in Proc. of the 2008 International Joint Conference on Neural Networks (IJCNN 2008), Hong Kong, China, Jun. 2008, pp. 3555-3562.


162. Van Belle V., Pelckmans K., Suykens J.A.K., Van Huffel S., "Survival SVM: a Practical Scalable Algorithm", in Proc. of the 16th European Symposium on Artificial Neural Networks (ESANN2008), Bruges, Belgium, Apr. 2008, pp. 89-94.


163. Karsmakers P., Leroux P., De Brabanter J., Suykens J.A.K., "Least Squares Support Vector Machines for Modelling Electronic Devices", in Proc. of the European Conference on the Use of Modern Information and Communication Technologies (ECUMICT), Ghent, Belgium, Mar. 2008, pp. 1-4.


164. Van Calster B., Luts J., Suykens J.A.K., Condous G., Bourne T., Timmerman D., Van Huffel S., "Comparing methods for multi-class probabilities in medical decision making using LS-SVMs and kernel logistic regression", in Artificial neural networks - ICANN 2007, (Marques de Sa J., Alexandre L.A., Duch W., and Mandic D., eds.), Proc. of the 17th International Conference on Artificial Neural Networks, Porto (Portugal), 9-13 September 2007, vol. 4669 of Lecture Notes in Computer Science, Springer, 2007, pp. 139-148.


165. Van Belle V., Pelckmans K., Suykens J.A.K., Van Huffel S., "Support vector machines for survival analysis", in Proc.of the Third International Conference on Computational Intelligence in Medicine and Healthcare (CIMED2007), Plymouth, England, Jul. 2007.


166. Pelckmans K., Suykens J.A.K., "topranking: predicting the most relevant element of a set", in Proc. of the 1st IAPR Workshop on Cognitive Information Processing (CIP 2008), Greece, Santorini, Jun. 2008.


167. Karsmakers P., Pelckmans K., Suykens J.A.K., Van hamme H., "Fixed-Size Kernel Logistic Regression for Phoneme Classification", in Proc. of the interspeech (INTERSPEECH), Antwerp, Belgium, Aug. 2007, pp. 78-81.


168. Alzate C., Suykens J.A.K., "ICA Through an LS-SVM Based Kernel CCA Measure for Independence", in Proc. of the 2007 International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL. U.S.A., Aug. 2007, pp. 2920-2925.


169. Ojeda F., Suykens J.A.K, De Moor B., "Variable selection by rank-one updates for least squares support vector machines", in Proc. of the 2007 International Joint Conference on Neural Networks (IJCNN), Orlando, FL. U.S.A., Aug. 2007, pp. 2283-2288.


170. Karsmakers P., Pelckmans K., Suykens J.A.K., "Multi-class kernel logistic regression: a fixed-size implementation", in Proc. of the international joint conference in neural networks (IJCNN), Orlando, Florida, Aug. 2007, pp. 1756-1761.


171. Pelckmans K., Suykens J.A.K., De Moor B., "Convex Optimization for the Design of Learning Machines", in Proc. of the European Symposium on Artificial Neural Networks (ESANN 2007), Bruges, Belgium, Apr. 2007, pp. 193-204.


172. Pelckmans K., Suykens J.A.K., De Moor B., "A Risk Minimization Principle for a Class of Parzen Estimators", in Proc. of the Neural Information Processing Systems (NIPS 2007), Vancouver, Canada, Dec. 2007.


173. Alzate C., Suykens J.A.K., "Image Segmentation using a Weighted Kernel PCA Approach to Spectral Clustering", in Proc. of the 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing (CIISP 2007), Honolulu, U.S.A., Apr. 2007, pp. 208-213.


174. Pelckmans K., Shawe-Taylor J., Suykens J.A.K., De Moor B., "Margin based Transductive Graph Cuts using Linear Programming", in Proc. of the 11th International Conference on Artificial Intelligence (AISTATS 2007), San Juan, Puerto Rico, Apr. 2007, pp. 360-367.


175. Vandewalle J., Suykens J.A.K., De Moor B., Lendasse A., "State of the art and evolutions in public data sets and competitions for system identification, time series prediction and pattern recognition", in Proc. of the 2007 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007), Honolulu, Hawai, Apr. 2007, pp. IV-1269 -- IV1272.


176. Pelckmans K., Suykens J.A.K., De Moor B., "The Kingdom-Capacity of a Graph: On the Difficulty of Learning a Graph Labelling", in Proc. of the workshop Mining and Learning with graphs (MLG 2006), Berlin, Germany, Sep. 2006.


177. Xavier de Souza S., Van Dyck M., Suykens J.A.K., Vandewalle J., "Fast and Robust Face Tracking for CNN chips: application to wheelchair driving", in Proc. of the 10th IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA'2006), Istanbul, Turkey, Aug. 2006, pp. 200-205.


178. Hillier D., Xavier de Souza S., Suykens J.A.K., Vandewalle J., "CNNOPT : Learning dynamics and CNN chip-specific robustness", in Proc. of the 10th IEEE International Workshop on Cellular Neural Networks and their applications (CNNA'2006), Istanbul, Turkey, Aug. 2006, pp. 114-119.


179. Yalcin M., Suykens J.A.K., Vandewalle J., "Multi-Scroll and Hypercube Attractors from Josephson Junctions", in Proc. of the ISCAS (ISCAS 2006), Cos, Greece, May 2006, pp. 718-721.


180. Pelckmans K., Van Vooren S., Coessens B., Suykens J.A.K., De Moor B., "Mutual Spectral Clustering: Microarray Experiments Versus Text Corpus", in Proc. of the workshop on Probabilistic Modeling and Machine Learning in Structural and Systems Biology (PMSB 2006), Helsinki, Finland, Jun. 2006.


181. Hillier D., Gunel S., Suykens J.A.K., Vandewalle J., "Learning Partial Synchronization Regimes with Imposed Qualitative Behavior on an Array of Chua's Oscillators", in Proc. of the International Symposium on Nonlinear Theory and its Applications (NOLTA 2006), Bologna, Italy, Sep. 2006, pp. 983-986.


182. Gunel S., Suykens J.A.K., Vandewalle J., "Global Optimization with Coupled Local Minimizers Excited by GaussianWhite Noise", in Proc. of the 2006 International Symposium on Nonlinear Theory and its Applications (NOLTA2006), Bologna, Italy, Sep. 2006, pp. 743-746.


183. Pelckmans K., Karsmakers P., Suykens J.A.K., De Moor B., "Ordinal Least Squares Support Vector Machines - a Discriminant Analysis Approach", in Proc. of the Machine Learning for Signal Processing (MLSP 2006), Maynooth, Ireland, Sep. 2006.


184. Xavier de Souza S., Suykens J.A.K., Vandewalle J., Bolle D., "Cooperative Behavior in Coupled Simulated Annealing Processes with Variance Control", in Proc. of the International Symposium on Nonlinear Theory and its Applications (NOLTA2006) (NOLTA2006), Bologna, Italy, Sep. 2006, pp. 879-882.


185. Alzate C., Suykens J.A.K., "A Weighted Kernel PCA Formulation with Out-of-Sample Extensions for Spectral Clustering Methods", in Proc. of the 2006 International Joint Conference on Neural Networks (IJCNN'06), Vancouver, Canada, Jul. 2006, pp. 138-144.


186. De Brabanter J., Pelckmans K., Suykens J.A.K., De Moor B., "Prediction Intervals for NAR Model Structures Using a Bootstrap Method", in Proc. of the 2005 International Symposium on Nonlinear Theory and its Applications (NOLTA 2005), Brugge, Belgium, Sep. 2005, 6 p.


187. De Brabanter J., Pelckmans K., Suykens J.A.K., De Moor B., "generalized likelihood ratio statistics based on bootstrap techniques for autoregression models", in Proc. of the 14th IFAC Symposium on System Identification (SYSID 2006), Newcastle, Australia, Mar. 2006, 6 p.


188. De Brabanter J., Pelckmans K., Suykens J.A.K., De Moor B., "Nonparametric Comparison of Signals Based on Statistical Bootstrap Techniques", in Proc. of the European Conference on the Use of Modern Information and Communication Technologies (ECUMICT), Gent, Belgium, Mar. 2006, pp. .


189. Pelckmans K., Suykens J.A.K., De Moor B., "Clustering and Staircases", in NIPS 2005 workshop on Theoretical Foundations on Clustering, 2 p.


190. Pluymers B., Kothare M.V., Suykens J.A.K., De Moor B., "Robust Constrained Linear State Feedback Synthesis using LMI's and Polyhedral Invariant Sets", in Proc. of the American Control Conference 2006 (ACC'06), Minneapolis, USA, Jun. 2006, pp. CD-ROM.


191. Imsland L., Rossiter J.A., Pluymers B., Suykens J.A.K., "Robust Triple Mode MPC", in Proc. of the American Control Conference 2006 (ACC'06), Minneapolis, USA, Jun. 2006, pp. CD-ROM.


192. Espinoza M., Suykens J.A.K., De Moor B., "LS-SVM Regression with Autocorrelated Errors", in Proc. of the 14th IFAC Symposium on System Identification (SYSID), Newcastle, Australia, Mar. 2006, pp. 582-587.


193. Xavier de Souza S., Suykens J.A.K., Vandewalle J., "Learning wave phenomena on the CNN universal machine", in Proc. of the 2005 International Symposium on Nonlinear Theory and its Applications (NOLTA'05), Bruges, Belgium, Oct. 2005.


194. Pochet N.L.M.M., Janssens F.A.L., De Smet F., Marchal K., Vergote I.B., Suykens J.A.K., De Moor B.L.R., "M@CBETH: optimizing clinical microarray classification", in Proc. of the 2005 IEEE Computational Systems Bioinformatics Conference (CSB2005), Stanford, California (US), Aug. 2005, pp. 89-90.


195. Espinoza M., Suykens J.A.K., De Moor B., "Short Term Chaotic Time Series Prediction using Symmetric LS-SVM Regression", in Proc. of the 2005 International Symposium on Nonlinear Theory and Applications (NOLTA), Bruges, Belgium, Oct. 2005, pp. 606-609.


196. Pelckmans K., De Brabanter J. Suykens J.A.K., De Moor B., "Convex Clustering Shrinkage", in Workshop on Statistics and Optimization of Clustering Workshop (PASCAL), London, U.K., Jul. 2005, pp. .


197. Ameye L., De Brabanter J., Suykens J.A.K., Cadron I., Devlieger R., Timmerman D., Spitz B., Van Huffel S., "Predictive Models for Long Term Survival after Premature Rupture of Membranes", in Proc. of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference (IEEE-EMB), Shanghai, China, Sep. 2005, pp. 572-575.


198. Pelckmans K., Suykens J.A.K., De Moor B., "Componentwise Support Vector Machines for Structure Detection", in Proc. of the International Conference on Artificial Neural Networks (ICANN 2005), Warsaw, Poland, Sep. 2005, pp. .


199. Pelckmans K., Goethals I., Suykens J.A.K., De Moor B., "On model complexity control in identification of Hammerstein Systems", in Proc. of the 44th IEEE conference on Decision and Control, and the European Control Conference (CDC-ECC 2005), (CDC-ECC 2005), Sevilla, Spain, Dec. 2005, pp. .


200. Goethals I., Pelckmans K., Hoegaerts L., Suykens J.A.K., De Moor B., "Subspace intersection identification of Hammerstein-Wiener systems", in Proc. of the 44th IEEE conference on Decision and Control, and the European Control Conference (CDC-ECC 2005), Seville, Spain, Dec. 2005, pp. 7108-7113.


201. Espinoza M., Suykens J.A.K., De Moor B., "Imposing Symmetry in Least Squares Support Vector Machines Regression", in Proc. of the 44th IEEE Conference on Decision and Control (CDC), Seville, Spain, Dec. 2005, pp. 5716 - 5721.


202. Alzate C., Suykens J.A.K., "Extending Kernel Principal Component Analysis to General Underlying Loss Functions", in Proc. of the International Joint Conference on Neural Networks (IJCNN'05), Montr?al, Canada, Jul. 2005, pp. 214-219.


203. Espinoza M., Suykens J.A.K., De Moor B., "Load Forecasting using Fixed-Size Least Squares Support Vector Machines", in Computational Intelligence and Bioinspired Systems, (Cabestany J., Prieto A., and Sandoval F., eds.), Proceedings of the 8th International Work-Conference on Artificial Neural Networks, vol. 3512 of Lecture Notes in Computer Science, Springer-Verlag, 2005, pp. 1018-1026.


204. D'hulst R., Verhaegen K., Barjas Blanco T., Suykens J.A.K., Driesen J., Vandewalle J., Belmans R., "Teaching in Green Power Production and Prediction of Electricity Consumption", in Proc. of the IEEE Power Engineering Society (IEEE-PES), San Francisco, USA, May 2005, pp. 6-8.


205. Rossiter J.A., Ding Y., Pluymers B., Suykens J.A.K., De Moor B., "Interpolation Based MPC with Exact Constraint Handling : the Uncertain Case", in Proc. of the Joint IEEE Conference on Decision and Control and European Control Conference (CDC-ECC05), Sevilla, Spain, Dec. 2005, pp. 302-307.


206. Arena P., Fortuna L., Frasca M., Vagliasindi G., Basile A., Yalcin M. E., Suykens J.A.K., "CNN wave based computation for robot navigation on ACE16K", in 2005 IEEE International Symposium on Circuits and Systems, 4 p.


207. Yalcin M.E., Suykens J.A.K., Vandewalle J., "Spatiotemporal pattern formation in the ACE16k CNN Chip", in Proc. of the 2005 IEEE International Symposium on Circuits and Systsems (ISCAS 2005), Kobe, Japan, May 2005, pp. 5814-5817.


208. Pluymers B., Rossiter J.A., Suykens J.A.K., De Moor B., "Interpolation Based MPC for LPV Systems using Polyhedral Invariant Sets", in Proc. of the American Control Conference 2005 (ACC2005), Portland, USA, Jun. 2005, pp. 810-815.


209. Rossiter J.A., Pluymers B., Suykens J.A.K., De Moor B., "A Multi Parametric Quadratic Programming Solution To Robust Predictive Control", in Proc. of the IFAC World Congress (IFAC05), Prague, Czech Republic, Jul. 2005.


210. Pluymers B., Rossiter J.A., Suykens J.A.K, De Moor B., "A Simple Algorithm for Robust MPC", in Proc. of the IFAC World Congress (IFAC05), Prague, Czech Republic, Jul. 2005, pp. CD-ROM.


211. Pluymers B., Rossiter J.A., Suykens J.A.K., De Moor B., "The Efficient Computation of Polyhedral Invariant Sets for Linear Systems with Polytopic Uncertainty", in Proc. of the American Control Conference 2005 (ACC2005), Portland, USA, Jun. 2005, pp. 804-809.


212. Pelckmans K., De Brabanter J., Suykens J.A.K, De Moor B., "Maximal Variation and Missing Values for Componentwise Support Vector Machines", in Proc. of the International Joint Conference on Neural Networks (IJCNN 2005), Montreal, Canada, Aug. 2005, pp. .


213. Verdult V., Suykens J.A.K., Boets J., Goethals I., De Moor B., "Least Squares Support Vector Machines for Kernel CCA in Nonlinear State-Space Identification", in Proc. of the 16th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2004), Leuven, Belgium, Jul. 2004.


214. Pelckmans K., Suykens J.A.K., De Moor B., "Alpha and beta stability for additively regularized LS-SVMs via convex optimization", in Proc. of the 16th International Symposium on Mathematical Theory of Networks and Systems (MTNS), Leuven, Belgium, Jul. 2004.


215. Hoegaerts L., Suykens J.A.K., Vandewalle J., De Moor B., "A comparison of pruning algorithms for sparse least squares support vector machines", in Proceedings of the 11th International Conference on Neural Information Processing (ICONIP 2004), Calcutta, India, vol. 3316, Nov. 2004, pp. 1247-1253.


216. Pelckmans K., Suykens J.A.K., De Moor B., "Morozov, Ivanov and Tikhonov regularization based LS-SVMs", in Proc. of the11th International Conference on Neural Information Processing (ICONIP 2004), Calcutta, India, Nov. 2004, pp. .


217. Espinoza M., Suykens J.A.K., De Moor B., "Partially Linear Models and Least Squares Support Vector Machines", in Proc. of the 43rd IEEE Conference on Decision and Control (CDC), Paradise City, Bahamas, Dec. 2004.


218. Pluymers B., Suykens J.A.K., De Moor B., "Robust finite-horizon MPC using optimal worst-case closed-loop predictions", in Proc. of the IEEE Conference on Decision and Control 2004 (CDC04), Paradise Island, Bahamas, Dec. 2004.


219. Xavier de Souza S., Suykens J.A.K., Vandewalle J., "Real-time tracking algorithm with locking on a given object for VLSI CNN-UM implementations", in Proc. of the 8th International Workshop on Cellular Neural Networks and their Applications (CNNA 2004), Budapest, Hungary, Jul. 2004, pp. 291-296.


220. Goethals I., Pelckmans K., Suykens J.A.K., De Moor B., "NARX identification of Hammerstein Models using least squares support vector machines", in Proc. of the 6th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2004), Stuttgart, Germany, Sep. 2004, pp. 507-512.


221. Pelckmans K., Suykens J.A.K., De Moor B., "Regularization constants in LS-SVMs : a fast estimate via convex optimization", in Proc. of the International Joint Conference on Neural Networks (IJCNN2004), Budapest, Hungary, Jul. 2004, pp. 699-704.


222. Hoegaerts L., De Lathauwer L., Suykens J.A.K., Vandewalle J., "Efficiently updating and tracking the dominant Kernel Eigenspace", in Proc. of the the 16th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2004), Leuven, Belgium, Jul. 2004.


223. Hoegaerts L., Suykens J.A.K., Vandewalle J., De Moor B., "Primal space sparse kernel partial least squares regression for large scale problems", in Proc. of the International Joint Conference on Neural Networks (IJCNN 2004), Hungary, Budapest, Jul. 2004, pp. 561-566.


224. Pluymers B., Suykens J.A.K., De Moor B., "Linear MPC with Time-Varying terminal cost using sparse convex combinations and bisection searching", in Proc. of the IEEE Conference on Decision and Control 2004 (CDC04), Paradise Island, Bahamas, Dec. 2004.


225. Yalcin M.E., Suykens J.A.K., Vandewalle J., "Experimental observation of autowaves on the ACE16k CNN chip", in Proc. of the 8th IEEE International Workshop on Cellular Neural Networks and their applications (CNNA 2004), Budapest, Hungary,, Jul. 2004, pp. 172-177.


226. Espinoza M., Pelckmans K., Hoegaerts L., Suykens J.A.K., De Moor B., "A comparative study of LS-SVMs applied to the Silver box identification problem", in Proc. of the 6th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2004), Stuttgart, Germany, Sep. 2004.


227. Pelckmans K., Suykens J.A.K., De Moor B., "Sparse LS-SVMs using additive Regularization with a penalized validation criterion", in Proc. of the 12e European Symposium on Artificial Neural Networks, Brugge, Belgium, Apr. 2004, pp. 435-440.


228. Yalcin M.E., Suykens J.A.K., Vandewalle J., "A double scroll based true random bit generator", in Proc. of the 2004 IEEE International Symposium on Circuits and Systems (ISCAS), Vancouver, Canada, May 2004, pp. IV-581-IV-584.


229. De Bie T., Suykens J.A.K., De Moor B., "Learning from general label constraints", in Proc. of the joint IAPR international workshops on Syntactical and Structural Pattern Recognition and Statistical Pattern Recognition (SSSPR 2004), Lisbon, Portugal,, vol. 3138, Aug. 2004, pp. 671-679.


230. Pelckmans K., De Brabanter J., Suykens J.A.K., De Moor B., "Variogram based noise variance estimation and its use in Kernel Based Regression", in Proc. of the IEEE Workshop on Neural Networks for Signal Processing, pp. 199-208.


231. Xavier de Souza S., Yalcin M.E., Suykens J.A.K., Vandewalle J., "Automatic chip-spcific CNN template optimization using adaptive simulated Annealing", in Proc. of the 16th European Conference on Circuit Theory and Design (ECCTD'03), Cracow, Poland, sep. 2003, pp. II-329---II-332.


232. Espinoza M., Suykens J.A.K., De Moor B., "Least Squares Support Vector Machines and Primal Space Estimation", in Proc. of the IEEE 42nd Conference on Decision and Control (CD-ROM), Maui, USA, Dec. 2003, pp. 3451-3456.


233. Hamers B., Suykens J.A.K., Leemans V., De Moor B., "Ensemble Learning of Coupled Parmeterised Kernel Models", in Supplementary Proc. of the International Conference on Artificial Neural Networks and International Conference on Neural Information Processing (ICANN/ICONIP), Istanbul, Turkey, Jun. 2003, pp. 130-133.


234. Hoegaerts L., Suykens J.A.K., Vandewalle J., De Moor B., "Kernel PLS variants for regression", in Proc. of the 11th European Symposium on Artificial Neural Networks, Bruges, Belgium, Apr. 2003, pp. 203-208.


235. Goethals I., Van Gestel T., Suykens J.A.K., Van Dooren P., De Moor B., "Identifying positive real models in subspace identification by using regularization", in Proc. of the 13th System Identification Symposium (SYSID2003), Rotterdam, Nederland, Aug. 2003, pp. 1411-1416.


236. Pluymers B., Roobrouck L., Buijs J., Suykens J.A.K., De Moor B., "An LMI-based constrained MPC scheme with time-varying terminal cost", in Proc. of the International Symposium on Advanced Control of Chemical Processes 2003 (ADCHEM03), Hong Kong, China, Jan. 2004, pp. CD-ROM.


237. De Brabanter J., Pelckmans K., Suykens J.A.K., De Moor B., Vandewalle J., "Robust complexity criteria for nonlinear regression in NARX models", in Proc. of the 13th System Identification Symposium (SYSID2003), Rotterdam, Nederland, Aug. 2003, pp. 79-84.


238. Hoegaerts L., Suykens J.A.K., Vandewalle J., De Moor B., "Subspace regression in reproducing kernel Hilbert space", in Proc. of the 13th System Identification Symposium (SYSID2003), Rotterdam, Nederland, Aug. 2003, pp. 839-842.


239. Suykens J.A.K., Yalcin M.E., Vandewalle J., "Coupled Chaotic Simulated Annealing Processes", in Proc. of the 2003 IEEE International Symposium on Circuits and Systems (ISCAS), Bangkok, Thailand, May 2003, pp. II-582--III-585.


240. Lu C., Van Gestel T., Suykens J.A.K., Van Huffel S., Vergote I., Timmerman D., "Classification of ovarian tumor using Bayesian least squares support vector machines", in Artificial Intelligence in Medicine, (Dojat M., Keravnou E., and Barahona P., eds.), Proc. of the 9th Conference on Artificial Intelligence in Medicine in Europe (AIME 2003), Protaras, Cyprus, October 2003, vol. 2780 of Lecture Notes in Artificial Intelligence, Springer-Verlag, 2003, pp. 219-228.


241. Van Gestel T., Espinoza M., Suykens J.A.K., De Moor B., "Bayesian input selection for nonlinear regression with LS-SVMs", in Proc. of the 13th System Identification Symposium (SYSID2003), Rotterdam, Nederland, Aug. 2003, pp. 578-583.


242. Ameye L., Van Huffel S., De Brabanter J., Suykens J.A.K., Spitz B., Cadron I., Devlieger R., Timmerman D., "Study of rupture of membranes before 26 weeks of gestation", in Proc. of the 2nd European Medical and Biological Engineering Conference (EMBEC'02), Vienna, Austria, Dec. 2002, pp. 760-761.


243. Lukas L., Devos A., Suykens J.A.K., Vanhamme L., Van Huffel S., Tate A.R., Majos C., Arus C., "The use of LS-SVM in the classification of brain tumors base on 1H-MR spectroscopy signals", in Proceedings of IEE Workshop 'Medical Applications of Signal Processing', Savoy Place, London, UK, Oct. 7, 2002, pp. 15/1-15/5.


244. Van Gestel T., Baesens B., Suykens J.A.K., Espinoza M., Baestaens D., Vanthienen J., De Moor B., "Bankruptcy Prediction with Least Squares Support Vector Machine Classifiers", in Proc. of the International Conference on Computational Intelligence for Financial Engineering (CIFER'03), Hong Kong, China, Mar. 2003, pp. 1-8.


245. Lu C., Van Gestel T., Suykens J.A.K., Van Huffel S., Vergrote I., Timmerman D., "Bayesian Least Squares Support Vector Machines for Classification of Ovarian Tumors", in Proc. of the 16th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2004), Leuven, Belgium, Jul. 2004.


246. Teughels A., De Roeck G., Suykens J.A.K., "Global optimization in FEM updating by coupled local minimizers", in Proc. of the International Conference on Noise and Vibration Engineering (ISMA 2002), Leuven, Belgium, Sep. 2002.


247. Yalcin M.E., Ozoguz S., Suykens J.A.K. and Vandewalle J., "Scroll maps from n-scroll attractors", in Proc. of the 10th International Workshop on Nonlinear Dynamics of Electronic Systems (NDES 2002), Izmir, Turkey, Jun. 2002, pp. 45-48.


248. De Brabanter J., Pelckmans K., Suykens J.A.K., Vandewalle J., "Robust cross-validation score function for non-linear function estimation", in Proc. of the International Conference on Artificial Neural Networks (ICANN 2002), Madrid, Spain, Aug. 2002, pp. 713-719.


249. Hamers B., Suykens J.A.K., De Moor B., "Compactly supported RBF kernels for sparsifying the Gram matrix in LS-SVM regression models", in Proceedings ICANN 2002, Madrid, Spain, Aug. 2002, pp. 720-726.


250. Ameye L., Lu C., Lukas L., De Brabanter J., Suykens J.A.K., Van Huffel S., Daniels H., Naulaers G., Devlieger H., "Prediction of mental development of preterm newborns at birth time using LS-SVM", in Proc. of the European Symposium on Artificial Neural Networks (ESANN'2002), Bruges, Belgium, Apr. 2002, pp. 167-172.


251. Teughels A., De Roeck G., Suykens J.A.K., "CLM, a global optimisation method applied to FEM updating", in Proc. of the 5th European Conference on Structural Dynamics (EURODYN 2002), Munich, Germany, Sep. 2002, pp. 1555-1560.


252. Lukas L., Devos A., Suykens J.A.K., Vanhamme L., Van Huffel S., Tate A., Majos C., Arus C., "The use of LS-SVM in the classification of brain tumors based on magnetic resonance spectroscopy signals", in Proc. of the European Symposium Artificial Neural Networks (ESANN'2002), Bruges, Belgium, Apr. 2002, pp. 131-136.


253. Van Gestel T., Suykens J.A.K., De Moor B., Vandewalle J., "Bayesian inference for LS-SVMs on large data sets using the Nystrom method", in Proc. of the World Congress on Computational Intelligence - International Joint Conference on Neural Networks (WCCI-IJCNN 2002), Honolulu, USA, May 2002, pp. 2779-2784.


254. Suykens J.A.K., Vandewalle J., "Coupled local minimizers : alternative formulations and extensions", in Proc. of the World Congress on Computational Intelligence- International Joint Conference on Neural Networks (WCCI-IJCNN 2002), Honolulu, USA, May 2002, pp. 2039-2043.


255. Teugels A., De Roeck G., Suykens J.A.K., "Updating of finite element models using coupled local minimizers (CLM)", in Proc. of the 3rd International Conference on Identification in Engineering Systems, Swansea, UK, Apr. 2002, pp. 280-289.


256. Lukas L., Suykens J.A.K., Vandewalle J., "Least squares support vector machines classifiers : a multi two-spiral benchmark problem", in Proc. of the Indonesian Student Scientific Meeting (ISSM 2001), Manchester, United Kingdom, Aug. 2001, pp. 289-292.


257. Van Gestel T., Suykens J.A.K., Lanckriet G., Lambrechts A., Baestaens D., De Moor B., Vandewalle J., "Bayesian interpretation of least squares support vector machines for financial time series prediction", in Proc. of the 5th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2001), Orlando, Florida, Jul. 2001, pp. 254-259.


258. Van Gestel T., Suykens J.A.K., De Brabanter J., De Moor B., Vandewalle J., "Kernel Canonical Correlation Analysis and Least Squares Support Vector Machines", in Proc. of the International Conference on Artificial Neural Networks (ICANN 2001), Vienna, Austria, Aug. 2001, pp. 381-386.


259. Suykens J.A.K., "Nonlinear modelling and support vector machines", in Proc. of the IEEE International Conference on Instrumentation and Measurement Technology (IEEE-IMTC 2001 State-of-the-Art lecture), Budapest, Hungary, May 2001, pp. 287-294.


260. Suykens J.A.K., Vandewalle J., "CNNs, Intelligence and Synchronization Theory", in Proc. of the 15th European Conference on Circuit Theory and Deisgn (ECCTD'01), Helsinki, Finland, Aug. 2001, pp. II-273-II-276.


261. Yalcin M., Ozoguz S., Suykens J.A.K., Vandewalle J., "3D-Grid Scroll Attractors", in Proc. of the 15th European Conference on Circuit Theory and Design (ECCTD'01), Helsinki, Finland, Aug. 2001, pp. III-425-III-428.


262. Yalcin M., Ozoguz S., Suykens J.A.K., Vandewalle J., "2D-Grid Scroll Attractors", in Proc. of the 9th Workshop on Nonlinear Dynamics of Electronic systems (NDES 2001), Delft, The Netherlands, Jun. 2001, pp. 181-184.


263. Suykens J.A.K., "Learning and generalization by coupled local minimizers", in Proc. of the International Joint Conference on Neural Networks - invited talk (IJCNN'01), Washington DC, USA, Jul. 2001, pp. 337-341.


264. Van Gestel T., Suykens J.A.K., De Brabanter J., De Moor B., Vandewalle J., "Least squares support vector machine regression for discriminant analysis", in Proc. of the International Joint Conference on Neural Networks (IJCNN'01), Washington DC, USA, Jul. 2001, pp. 2445-2450.


265. Van Gestel T., Suykens J.A.K., De Moor B. Vandewalle J., "Automatic relevance determination for Least squares support vector machine regression", in Proc. of the International Joint Conference on neural networks (IJCNN'01), Washington DC, USA, Jul. 2001, pp. 2416-2421.


266. Buijs J., Suykens J.A.K., De Moor B., "Model Predictive Control : Convex Optimization Approaches Versus Constrained Dynamic Backpropagation", in Proc. of the IFAC Conference on New Technologies for Computer Control (IFAC-NTCC2001), Hong Kong, Hong Kong, Nov. 2001, 8 p.


267. Van Gestel T., Suykens J.A.K., De Moor B., Vandewalle J., "Automatic relevance determination for least squares support vector machine classifiers", in Proc. of the European Symposium on Artificial Neural Networks (ESANN'2001), Bruges, Belgium, Apr. 2001, pp. 13-18.


268. Yalcin M.E., Suykens J.A.K., Vandewalle J., "Mutual synchronization of Time Delay Lur'e systems", in Proc. of the 2000 International Symposium on Nonlinear Theory and its Applications (NOLTA 2000), Dresden, Germany, Sep. 2000, pp. 241-244.


269. Baesens B., Viaene S., Van Gestel T., Suykens J.A.K., Dedene G., De Moor B., Vanthienen J., "An Empirical assessment of Kernel Type Performance for Least Squares Support Vector Machine Classifiers", in Proc. of the Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies (KES2000), Brighton, UK, Aug. 2000, 4 p.


270. Schouten T., Suykens J.A.K., De Moor B., "Multi resolution least squares SVM solver", in Proc. of the 43rd IEEE Midwest Symposium on Circuits and Systems (MWSCAS2000), Lansing, Michigan, Aug. 2000, cdrom p.


271. Govoreanu B., Suykens J.A.K., Schoenmaker W., Amza C., Vandewalle J., Profirescu M., "A comparison between various empirical models for TCAD purposes", in Proceedings of the 23rd International Semiconductor Conference, Sinaie, Romania, 2000, pp. 315-318.


272. Baesens B., Viaene S., Van Gestel T., Suykens J.A.K., Van den Poel D., Vanthienen J., De Moor B., Dedene G., "Knowledge discovery using least squares support vector machine classifiers : a direct marketing case", in Proc. PKDD2000, Fourth European Conference on Principles of Knowledge Discovery in Databases, Lyon, France, Lyon, France, Sep. 2000, pp. 657-664.


273. Van Gestel T., Suykens J.A.K., Van Dooren P., De Moor B., "Imposing stability in subspace identification by regularization", in Proc. of the 39th IEEE Conference on Decision and Control (CDC2000), Sydney, Australia, Dec. 2000, pp. 1555-1560.


274. Viaene S., Baesens B., Van Gestel T., Suykens J.A.K., Dedene D., De Moor B., Vanthienen J., "Least squares support vector machine classifiers : An empirical evaluation", in Proc. of the 12th Belgian-Dutch Artificial Intelligence Conference (BNAIC'00), Kaatsheuvel, The Netherlands, Nov. 2000, pp. 69-79.


275. Yalcin M., Suykens J.A.K., Vandewalle J., "Hyperchaotic n-scroll attractors", in Proc. of the IEEE Workshop on Nonlinear Dynamics of Electronic System (NDES 2000), Catania, Italy, May 2000, pp. 25-28.


276. Suykens J.A.K., Vandewalle J., "The K.U.Leuven competition data : a challenge for advanced neural network techniques", in Proc. of the European Symposium on Artificial Neural Networks (ESANN'2000), Bruges, Belgium, 2000, pp. 299-304.


277. Suykens J.A.K., Lukas L., Vandewalle J., "Sparse Least Squares Support Vector Machine Classifiers", in Proc. of the European Symposium on Artificial Neural Networks (ESANN'2000), Bruges, Belgium, 2000, pp. 37-42.


278. Duhoux M., Suykens J.A.K., Mignon J., Vandewalle J., De Moor B., "Improved long-term temperature prediction for a blast furnace using neural networks", in Proc. of the ICSC Symposia on Engineering of Intelligent Systems (EIS2000), Paisley, UK, Jun. 2000, pp. 40-48.


279. Suykens J.A.K., Lukas L., Vandewalle J., "Sparse approximation using least squares support vector machines", in Proc. of the IEEE International Symposium on Circuits and Systems (ISCAS 2000), Geneva, Switzerland, May 2000, pp. II757-II760.


280. Suykens J.A.K., Vandewalle J., "Generalized Synchronization : a Lagrange Programming Network Formulation", in Proc. of the International Symposium on Nonlinear Theory and its Applications (NOLTA'99), Waikoloa, Hawaii, USA, Dec. 1999, pp. 21-24.


281. Suykens J.A.K., Vandewalle J., "Synchronization theory of Lur'e systems : an overview", in Proc. of the International Workshop on nonlinear dynamics of electronic systems (keynote talk) (NDES'99), Ronne, Denmark, Jul. 1999, pp. 247-252.


282. Yalcin M.E., Suykens J.A.K., Vandewalle J., "Experimental confirmation of a nonlinear Hinfty synchronization scheme with 5-scroll attractors", in Proc. of the International Workshop on nonlinear dynamics of electronic systems (NDES'99), Ronne, Denmark, Jul. 1999, pp. 257-260.


283. Van Gorp J., Suykens J.A.K., "Representation of SISO Fuzzy logic control systems within NLq theory", in Proc. of Smart Engineering System Design (ANNIE'99), St. Louis, Missouri USA, Nov. 1999, pp. 603-609.


284. Suykens J.A.K., Lukas L., Van Dooren P., De Moor B., Vandewalle J., "Least squares support vector machine classifiers : a large scale algorithm", in Proc. of the European Conference on Circuit Theory and Design (ECCTD'99), Stresa, Italy, Sep. 1999, pp. 839-842.


285. Yalcin M.E., Suykens J.A.K., Vandewalle J., "Experimental confirmation of nonlinear Hinfty synchronization for Chua's circuit", in Proc. of the European Conference on Circuit Theory and Design (ECCTD'99), Stresa, Italy, Sep. 1999, pp. 177-180.


286. Suykens J.A.K., Vandewalle J., "Continuous time NLq Theory : absolute stability criteria", in Proc. of the International Joint Conference on Neural Networks (IJCNN'99), Washington DC, USA, Jul. 1999, pp. CD-rom.


287. Suykens J.A.K., Vandewalle J., "Multiclass Least Squares Support Vector Machines", in Proc. of the International Joint Conference on Neural Networks (IJCNN'99), Washington DC, USA, Jul. 1999, pp. CD-rom.


288. Yalcin M.E., Suykens J.A.K., Vandewalle J., "On the realization of n-scroll attractors", in Proc. of the IEEE International Symposium on Circuits and Systems (ISCAS'99), Orlando, Florida, May 1999, pp. 483-486.


289. Verrelst H., Suykens J.A.K., Vandewalle J., De Moor B., "Bayesian Learning and the Fokker-Planck machine", in Proc. of the International Workshop on Advanced Black-box Techniques for Nonlinear Modeling, Leuven, Belgium, Jul. 1998, pp. 55-61.


290. Suykens J.A.K., Yang T., Vandewalle J., Chua L.O., "Impulsive synchronization of Lur'e systems: state feedback case", in Proc. of the IEEE Conference on Decision and Control (CDC'98), Tampa, Florida, Dec. 1998, pp. 1963-1966.


291. Suykens J.A.K., Vandewalle J., "Improved generalization ability of neurocontrollers by imposing NLq stability constraints", in Proc. of the European Symposium on Artificial Neural Networks (ESANN'98), Brugge, Belgium, Apr. 1998, pp. 99-104.


292. Suykens J.A.K., Vandewalle J., Chua L.O., "Nonlinear H$_{\infty}$ synchronization : case study for a hyperchaotic system", in Proc. of the IEEE International Symposium on Circuits and Systems (ISCAS'98), Montery, California USA, Jun. 1998.


293. Suykens J.A.K., Munuzuri A.P., Vandewalle J., Chua L.O., "Phenomena from CNNs with generalized Chua's circuits", in Proc. of the International Symposium on Nonlinear Theory and its Applications (NOLTA 97), Honolulu, Hawaii USA, Dec. 1997, pp. 205-208.


294. Favoreel W., Lemmerling P., Suykens J.A.K., De Moor B., Crepel M., Briol P., "Modeling the Belgian gas consumption", in Proc. of the European Control Conference (ECC97), Brussels, Belgium, Sep. 1997, 6 p.


295. Suykens J.A.K., De Moor B., Vandewalle J., "Robust NLq neural control theory", in Proc. of the International Conference on Neural Networks (ICCN'97), Houston, Texas USA, Jun. 1997.


296. Verrelst H., Van Acker K., Suykens J.A.K., Motmans B., De Moor B., Vandewalle J., "NL$_{q}$ neural control theory: case study for a ball and beam system", in Proc. of the European Control Conference (ECC'97), Brussels, Belgium, Jul. 1997.


297. Suykens J.A.K., Vandewalle J., De Moor B., "Nonlinear H$_{\infty}$ control for continuous-time recurrent neural networks", in Proc. of the European Control Conference (ECC'97), Brussels, Belgium, Jul. 1997.


298. Suykens J.A.K., Vandewalle J., "Absolute stability and dissipativity of continuous time multilayer recurrent neural networks", in Proc. of the 1997 IEEE International Symposium on Circuits and Systems (ISCAS'97), Hong Kong, Jun. 1997, pp. 517-520.


299. Suykens J.A.K., Vandewalle J., "Absolute stability criterion for a Lur'e problem with multilayer perceptron nonlinearity", in Proc. of the International Workshop on Nonlinear Dynamics of Electronic Systems (NDES'96), Sevilla, Spain, Jun. 1996, pp. 117-122.


300. Suykens J.A.K., De Moor B., Vandewalle J., "Links between NL$_{q}$ neural control theory and mu robust control theory", in Proc. of the IEEE International Conference on Decision and Control (CDC'96), Kobe, Japan, Dec. 1996.


301. Suykens J.A.K., Vandewalle J., "On the identification of a chaotic system by means of recurrent neural state space models", in Proc. of the IEEE International Conference on Neural Networks (ICNN'95), Perth, Australia, Nov.-Dec. 1995.


302. Suykens J.A.K., Vandewalle J., "Global asymptotic stability criteria for multilayer recurrent neural networks with applications to modeling and control", in Proc. of the IEEE International Conference on Neural Networks (ICNN'95), Perth, Australia, Nov.-Dec. 1995.


303. Suykens J.A.K., Vandewalle J., "NL$_{q}$ theory: a unifying framework for analysis, design and applications of complex nonlinear systems", in Proc. of the International Workshop on Non-Linear Dynamics of Electronic Systems (NDES'95) (Invited talk), Dublin, Ireland, Jul. 1995, pp. 121-130.


304. Suykens J.A.K., Vandewalle J., "Nonconvex optimization using a Fokker-Planck learning machine", in Proc. of the 12th European Conference on Circuit Theory, Design (ECCTD'95), Istanbul, Turkey, Aug. 1995, pp. 983-986.


305. Suykens J.A.K., De Moor B., Vandewalle J., "NL$_{q}$ theory: unifications in the theory of neural networks, systems and control", in Proc. of the European Symposium on Artificial Neural Networks (ESANN'95), Brussels, Belgium, Apr. 1995, pp. 271-276.


306. Suykens J.A.K., Vandewalle J., "Generalized cellular neural networks represented in the NL$_{q}$ framework", in Proc. of the IEEE International Symposium on Circuits and Systems (ISCAS'95), Seattle, Washington, 1995, pp. 645-648.


307. Suykens J.A.K., De Moor B., Vandewalle J., "Stability criteria for neural control systems", in Proc. of the 3rd European Control Conference (ECC'95), Roma, Italy, Sep. 1995, pp. 2766-2771.


308. Suykens J.A.K., De Moor B., Vandewalle J., "Neural network models as linear systems with bounded uncertainty, applicable to robust controller design", in Proc. of the International Symposium on Nonlinear Theory and its Application (NOLTA'93), Hawaii, Dec. 1993, pp. 419-422.


309. Suykens J.A.K., De Moor B., Vandewalle J., "Stabilizing neural controllers: a case study for swinging up a double inverted pendulum", in Proc. of the International Symposium on Nonlinear Theory and its Application (NOLTA'93), Hawaii, Dec. 1993, pp. 411-414.


310. Suykens J.A.K., Vandewalle J., "Between n-double sinks and n-double scrolls (n = 1,2,3,4,...)", in Proc. of the International Symposium on Nonlinear Theory and its Application (NOLTA'93), Hawaii, Dec. 1993, pp. 829-834.


311. Thierens D., Suykens J.A.K., Vandewalle J., De Moor B., "Genetic weight optimization of a feedforward neural network controller", in Proc. of the International Conference on Artificial Neural Networks and Genetic Algorithms, Innsbruck, Austria, Apr. 1993, 6 p.


312. Suykens J.A.K., De Moor B., "Nonlinear system identification using multilayer neural networks: some ideas for initial weights, number of hidden neurons and error criteria", in Proc. of the 12th IFAC World Congress, Sydney, Australia, Jul. 1993, pp. 49-52.


313. Van Overschee P., De Moor B., Suykens J.A.K., "Subspace algorithms for system identification and stochastic realization", in Proc. of the International Symposium on Recent Advances in Mathematical Theory of Systems, Control, Networks and Signal Processing (MTNS'91), Kobe, Japan, Jun. 1991, pp. 589-595.

National Conference Papers

1. De Cooman B., Suykens J.A.K., Ortseifen A., "Improving temporal smoothness of deterministic reinforcement learning policies with continuous actions", in 33rd Benelux Conference on Artificial Intelligence (BNAIC 2021), Esch-sur-Alzette, Luxembourg, Nov. 2021, pp. 217-240.

Ph.D.-theses

1. Suykens J.A.K., Artificial Neural Networks for Modeling and Control of Nonlinear Systems, PhD thesis, Faculty of Engineering, KU Leuven (Leuven, Belgium), May 1995, 249 p.

Internal Reports

1. Zeng X., Tao Q., Suykens J.A.K., "Feature Learning using Multi-view Kernel Partial Least Squares", Internal Report 24-64, ESAT-SISTA, KU Leuven (Leuven, Belgium), 2024. Accepted for publication in ESANN 2024.


2. Tao Q., Tonin F., Lambert A., Chen Y., Patrinos P., Suykens J.A.K., "Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nystrom method", Internal Report 24-61, ESAT-SISTA, KU Leuven (Leuven, Belgium), 2024. Accepted for publication in The 41st International Conference on Machine Learning (ICML), pp. 1-17.


3. Chen Y., Tao Q., Tonin F., Suykens J., "Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes", Internal Report 24-18, ESAT-SISTA, KU Leuven (Leuven, Belgium), 2024. Accepted for publication in ICML 2024.


4. Chowdhury S.R., Suykens J.A.K., "Equivariant Representation Learning with Equivariant Convolutional Kernel Networks", Internal Report 23-92, ESAT-SISTA, KU Leuven (Leuven, Belgium), 2023. Accepted for publication in 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML) at the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA, 2023..


5. Chen Y., Tao Q., Tonin F., Suykens J.A.K., "Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation", Internal Report 23-84, ESAT-SISTA, KU Leuven (Leuven, Belgium), 2023. Accepted for publication in NeurIPS 2023.


6. Achten S., Pandey A., De Meulemeester H., De Moor B., Suykens J.A.K., "Duality in Multi-View Restricted Kernel Machines", Internal Report 23-83, ESAT-SISTA, KU Leuven (Leuven, Belgium), 2023. ICML Workshop on Duality for Modern Machine Learning, Honolulu, Hawaii, USA, 2023.


7. De Plaen H., De Plaen P.-F., Suykens J. A. K., Proesmans M., Tuytelaars T., Van Gool L., "Unbalanced Optimal Transport: A Unified Framework for Object Detection", Internal Report 23-65, ESAT-SISTA, KU Leuven (Leuven, Belgium), 2023. Accepted for publication in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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