Deep Restricted Kernel Machines: Methods and Foundations

From 01-01-2017 to 31-12-2020

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.

Team

• Johan Suykens, Promoter

Financing

Funding: FWO - Research Foundation - Flanders

Program/Grant Type: FWO Project - FWO Research Project