Description - Learning Kernel Classifiers by Ralf Herbrich
Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This text provides a comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
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(229mm x 178mm x 28mm)
Publisher: MIT Press Ltd
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Book Reviews - Learning Kernel Classifiers by Ralf Herbrich
Author Biography - Ralf Herbrich
Ralf Herbrich is a Postdoctoral Researcher in the Machine Learning and Perception Group at Microsoft Research Cambridge and a Research Fellow of Darwin College, University of Cambridge.