Learning Kernel Classifiers

Theory and Algorithms

Ralf Herbrich

Buch (gebundene Ausgabe, Englisch)
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Beschreibung

An overview of the theory and application of kernel classification methods.

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.

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.

Produktdetails

Einband gebundene Ausgabe
Seitenzahl 384
Altersempfehlung ab 18 Jahr(e)
Erscheinungsdatum 01.12.2001
Sprache Englisch
ISBN 978-0-262-08306-5
Reihe Adaptive Computation and Machine Learning series
Verlag MIT Press
Maße (L/B/H) 23.4/18.3/2.7 cm
Gewicht 871 g

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