Learning kernel classifiers : theory and algorithms / Ralf Herbrich.
2002
Q325.5 .H48 2002eb
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Title
Learning kernel classifiers : theory and algorithms / Ralf Herbrich.
Author
Herbrich, Ralf.
ISBN
9780262256339 (electronic bk.)
0262256339 (electronic bk.)
0585436681 (electronic bk.)
9780585436685 (electronic bk.)
9780262083065
026208306X
0262256339 (electronic bk.)
0585436681 (electronic bk.)
9780585436685 (electronic bk.)
9780262083065
026208306X
Imprint
Cambridge, Mass. : MIT Press, ©2002.
Language
English
Description
1 online resource (xx, 364 pages) : illustrations.
Call Number
Q325.5 .H48 2002eb
Dewey Decimal Classification
006.3/1
Summary
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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OCLC-licensed vendor bibliographic record.
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