Learning Kernel Classifiers
Theory and Algorithms
Ralf Herbrich(Author)
MIT Press
Published on 14. May 2014
Online / Databases
384 pages
978-0-262-25633-9 (ISBN)
Article exhausted; check different version
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.
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.
More details
Series
Language
English
Place of publication
Cambridge, Mass.
United States
Publishing group
MIT Press Ltd
Target group
Professional and scholarly
Interest Age: From 18 years
Dimensions
Height: 229 mm
Width: 178 mm
Thickness: 25 mm
ISBN-13
978-0-262-25633-9 (9780262256339)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Book
12/2001
MIT Press
€49.56
Article exhausted; check different version