Learning Kernel Classifiers: Theory and Algorithms

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Learning Kernel Classifiers: Theory and Algorithms

Herbrich, Ralf. - Personal Name; Learning Kernel Classifiers: Theory and Algorithms - Personal Name;

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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Series Title: -
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Publisher: Machine learning.;Algorithms. : Cambridge, Mass. : MIT Press,., 2002
Collation: -
Language: English
ISBN/ISSN: 9780262256339
Classification: NONE
Content Type: text
Media Type: computer
Carrier Type: online resource
Edition: 1
Subject(s): Machine learning.;Algorithms.
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Cataloger: Suwardi
Source: https://direct.mit.edu/books/book/3806/Learning-Kernel-ClassifiersTheory-and-Algorithms
Validator: Suwardi
Digital Object Identifier (DOI): https://doi.org/10.7551/mitpress/4170.001.0001
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