Kernel Methods: A Paradigm for Pattern Analysis

Abstract

During the past decade, a major revolution has taken place in pattern-recognition technology with the introduction of rigorous and powerful mathematical approaches in problem domains previously treated with heuristic and less efficient techniques. The use of convex optimisation and statistical learning theory has been combined with ideas from functional analysis and classical statistics to produce a class of algorithms called kernel methods (KMs), which have rapidly become commonplace in applications. This book, and others, provides evidence of the practical applications that have made kernel methods a fundamental part of the toolbox for machine learning, statistics, and signal processing practitioners. The field of kernel methods has not only provided new insights and therefore new algorithms, but it has also created much discussion on well-established techniques such as Parzen windows and Gaussian processes, which use essentially the same technique but in different frameworks.