Learning Kernel Classifiers by Ralf Herbrich
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Learning Kernel Classifiers
By Ralf Herbrich

Learning Kernel Classifiers

Theory and Algorithms

By (author) See other recent books by Ralf Herbrich
Format: Hardback

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Learning Kernel Classifiers by Ralf Herbrich

Book Description

Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--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 book provides the first 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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Book Details

ISBN: 9780262083065
ISBN-10: 026208306X
Format: Hardback
(229mm x 178mm x 28mm)
Pages: 384
Imprint: MIT Press
Publisher: MIT Press Ltd
Publish Date: 22-Jan-2002
Country of Publication: United States

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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.

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