Learning kernel classifiers : theory and algorithms

著者

    • Herbrich, Ralf

書誌事項

Learning kernel classifiers : theory and algorithms

Ralf Herbrich

(Adaptive computation and machine learning)

MIT Press, c2002

  • : hc

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注記

Includes bibliographical references (p. [339]-355) and index

Pagination of 2nd reprint: xx, 366 p.

2nd reprint has different pagenation: bibliographical references (p. [341]-357)

内容説明・目次

内容説明

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.

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詳細情報

  • NII書誌ID(NCID)
    BA55162796
  • ISBN
    • 026208306X
  • LCCN
    01044445
  • 出版国コード
    us
  • タイトル言語コード
    eng
  • 本文言語コード
    eng
  • 出版地
    Cambridge, Mass.
  • ページ数/冊数
    xx, 364 p.
  • 大きさ
    24 cm
  • 分類
  • 件名
  • 親書誌ID
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