Financing: Research Foundation - Flanders (FWO)

Project reference Nr.: GOA4917N
Start: 2017-01-01
End: 2020-12-31

Description:

This research proposal entitled "Deep Restricted Kernel Machines: Methods and Foundations" is related to two main directions in the field of machine learning:- deep learning- support vector machines and kernel methods
This project aims at an in-depth study of the recently proposed "Deep Restricted Kernel Machines" (Deep RKM). A method of conjugate feature duality is used to obtain a representation in terms of visible and hidden units. In this way the class of restricted kernel machines can be linked to restricted Boltzmann machines, which do not contain hidden-to-hidden connections. Deep RKM is obtained by coupling the restricted kernel machines over different levels.The main objectives of the proposal are- to investigate the duality principles- to extend the class of restricted kernel machine models- to explore different coupling schemes and obtain efficient learning rules- to develop methods for large scale problems and big data.The project intends to achieve a new powerful class of machine learning techniques for supervised, unsupervised and semi-supervised learning, and contribute to setting new foundations both for deep learning and for support vector machines and kernel methods.

SMC people involved in the project:

• Johan Suykens (Promoter)