Learning Kernel Classifiers: Theory and Algorithms

Learning Kernel Classifiers: Theory and Algorithms

by Ralf Herbrich
     
 

ISBN-10: 026208306X

ISBN-13: 9780262083065

Pub. Date: 12/15/2001

Publisher: MIT Press

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

Overview

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.

Product Details

ISBN-13:
9780262083065
Publisher:
MIT Press
Publication date:
12/15/2001
Series:
Adaptive Computation and Machine Learning series
Edition description:
New Edition
Pages:
384
Sales rank:
1,012,023
Product dimensions:
7.00(w) x 9.00(h) x 1.10(d)
Age Range:
18 Years

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