Abstract:
Kernel Methods went through a period of rapid development in the 90′s emerging as the method of choice for a number of machine learning applications. The presentation will introduce the key ideas of the approach deriving some of the key algorithms including Ridge Regression, Support Vector Machines, and Kernel PCA. A light review of the basis of the methods in Statistical Learning Theory will be provided as will an introduction to kernel design strategies.
Bio:
John S Shawe-Taylor is a professor at University College London (UK) where he is Director of the Centre for Computational Statistics and Machine Learning (CSML). His main research area is Statistical Learning Theory, but his contributions range from Neural Networks, to Machine Learning, to Graph Theory.
John Shawe-Taylor obtained a PhD in Mathematics at Royal Holloway, University of London in 1986. He subsequently completed an MSc in the Foundations of Advanced Information Technology at Imperial College. He was promoted to Professor of Computing Science in 1996. He has published over 150 research papers. He moved to the University of Southampton in 2003 to lead the ISIS research group. He has been appointed the Director of the Centre for Computational Statistics and Machine Learning at University College, London from July 2006. He has coordinated a number of European wide projects investigating the theory and practice of Machine Learning, including the NeuroCOLT projects. He is currently the scientific coordinator of a Framework VI Network of Excellence in Pattern Analysis, Statistical Modelling and Computational Learning (PASCAL) involving 57 partners.