Learning kernel classifiers: theory and algorithms
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...
Gespeichert in:
| Beteilige Person: | |
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| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge, Mass.
MIT Press
©2002
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| Schriftenreihe: | Adaptive computation and machine learning
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| Links: | https://doi.org/10.7551/mitpress/4170.001.0001?locatt=mode:legacy |
| Zusammenfassung: | 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. |
| Umfang: | 1 Online-Ressource (xx, 364 Seiten) Illustrationen |
| ISBN: | 026208306X 0262256339 0585436681 9780262083065 9780262256339 9780585436685 |
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| spelling | Herbrich, Ralf Learning kernel classifiers theory and algorithms Ralf Herbrich Cambridge, Mass. MIT Press ©2002 1 Online-Ressource (xx, 364 Seiten) Illustrationen txt c cr Adaptive computation and machine learning 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. |
| spellingShingle | Herbrich, Ralf Learning kernel classifiers theory and algorithms |
| title | Learning kernel classifiers theory and algorithms |
| title_auth | Learning kernel classifiers theory and algorithms |
| title_exact_search | Learning kernel classifiers theory and algorithms |
| title_full | Learning kernel classifiers theory and algorithms Ralf Herbrich |
| title_fullStr | Learning kernel classifiers theory and algorithms Ralf Herbrich |
| title_full_unstemmed | Learning kernel classifiers theory and algorithms Ralf Herbrich |
| title_short | Learning kernel classifiers |
| title_sort | learning kernel classifiers theory and algorithms |
| title_sub | theory and algorithms |
| work_keys_str_mv | AT herbrichralf learningkernelclassifierstheoryandalgorithms |