Learning kernel classifiers :: theory and algorithms /

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...

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Bibliographic Details
Main Author: Herbrich, Ralf
Format: Electronic eBook
Language:English
Published: Cambridge, Mass. : MIT Press, ©2002.
Series:Adaptive computation and machine learning.
Subjects:
Online Access:DE-862
DE-863
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.
Physical Description:1 online resource (xx, 364 pages) : illustrations
Bibliography:Includes bibliographical references (pages 339-355) and index.
ISBN:9780262256339
0262256339
0585436681
9780585436685
9780262083065
026208306X

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