T7

Abstract:
This tutorial is presented at an intermediate level and seeks to explore the variety of ways in which modern kernel methods use the concept of similarity measures in Reproducing Kernel Hilbert Spaces (RKHS) to extend, strengthen and enrich existing learning machines while creating brand new ones. Techniques like kernel PCA which extends the ubiquitous method of principal component analysis are presented, as well as spectral clustering, kernel kMeans, and the whole suite of kernel regression techniques from Radial basis function regression to the Relevance vector Machine Regression, the Support vector Regression machine and the Gaussian Process Regression method, just to name a few. Examples involving small, medium and extremely large (Big data) datasets are used to illustrate the methods. The software environment used is R studio.

Presenter:
Ernest Fokoué, School of Mathematical Sciences, Rochester Institute of Technology, USA