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...
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cambridge, Mass. :
MIT Press,
©2002.
|
| Series: | Adaptive computation and machine learning.
|
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | 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. |
| Physical Description: | 1 online resource (xx, 364 pages) : illustrations |
| Bibliography: | Includes bibliographical references (pages 339-355) and index. |
| ISBN: | 9780262256339 0262256339 0585436681 9780585436685 9780262083065 026208306X |
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| 520 | |a 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. | ||
| 505 | 0 | 0 | |g 1. |t Introduction -- |g I. |t Learning Algorithms. |g 2. |t Kernel Classifiers from a Machine Learning Perspective. |g 3. |t Kernel Classifiers from a Bayesian Perspective -- |g II. |t Learning Theory. |g 4. |t Mathematical Models of Learning. |g 5. |t Bounds for Specific Algorithms -- |g III. |t Appendices -- |g A. |t Theoretical Background and Basic Inequalities -- |g B. |t Proofs and Derivations -- Part I -- |g C. |t Proofs and Derivations -- Part II -- |g D. |t Pseudocodes. |
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| contents | Introduction -- Learning Algorithms. Kernel Classifiers from a Machine Learning Perspective. Kernel Classifiers from a Bayesian Perspective -- Learning Theory. Mathematical Models of Learning. Bounds for Specific Algorithms -- Appendices -- Theoretical Background and Basic Inequalities -- Proofs and Derivations -- Part I -- Proofs and Derivations -- Part II -- Pseudocodes. |
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| discipline | Informatik |
| format | Electronic eBook |
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| indexdate | 2025-03-18T14:27:30Z |
| institution | BVB |
| isbn | 9780262256339 0262256339 0585436681 9780585436685 9780262083065 026208306X |
| language | English |
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| series | Adaptive computation and machine learning. |
| series2 | Adaptive computation and machine learning |
| spelling | Herbrich, Ralf. Learning kernel classifiers : theory and algorithms / Ralf Herbrich. Cambridge, Mass. : MIT Press, ©2002. 1 online resource (xx, 364 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file rdaft Adaptive computation and machine learning Includes bibliographical references (pages 339-355) and index. Print version record. 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. 1. Introduction -- I. Learning Algorithms. 2. Kernel Classifiers from a Machine Learning Perspective. 3. Kernel Classifiers from a Bayesian Perspective -- II. Learning Theory. 4. Mathematical Models of Learning. 5. Bounds for Specific Algorithms -- III. Appendices -- A. Theoretical Background and Basic Inequalities -- B. Proofs and Derivations -- Part I -- C. Proofs and Derivations -- Part II -- D. Pseudocodes. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Algorithms https://id.nlm.nih.gov/mesh/D000465 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Algorithmes. algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Algorithms fast Machine learning fast COMPUTER SCIENCE/Machine Learning & Neural Networks Electronic book. has work: Learning Kernel Classifiers (Text) https://id.oclc.org/worldcat/entity/E39PCXH7q9Mwgk8w7fcw7HBcCP https://id.oclc.org/worldcat/ontology/hasWork Print version: Herbrich, Ralf. Learning kernel classifiers. Cambridge, Mass. : MIT Press, ©2002 026208306X (DLC) 2001044445 (OCoLC)47705793 Adaptive computation and machine learning. |
| spellingShingle | Herbrich, Ralf Learning kernel classifiers : theory and algorithms / Adaptive computation and machine learning. Introduction -- Learning Algorithms. Kernel Classifiers from a Machine Learning Perspective. Kernel Classifiers from a Bayesian Perspective -- Learning Theory. Mathematical Models of Learning. Bounds for Specific Algorithms -- Appendices -- Theoretical Background and Basic Inequalities -- Proofs and Derivations -- Part I -- Proofs and Derivations -- Part II -- Pseudocodes. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Algorithms https://id.nlm.nih.gov/mesh/D000465 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Algorithmes. algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Algorithms fast Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85003487 https://id.nlm.nih.gov/mesh/D000465 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Learning kernel classifiers : theory and algorithms / |
| title_alt | Introduction -- Learning Algorithms. Kernel Classifiers from a Machine Learning Perspective. Kernel Classifiers from a Bayesian Perspective -- Learning Theory. Mathematical Models of Learning. Bounds for Specific Algorithms -- Appendices -- Theoretical Background and Basic Inequalities -- Proofs and Derivations -- Part I -- Proofs and Derivations -- Part II -- Pseudocodes. |
| 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 / |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Algorithms https://id.nlm.nih.gov/mesh/D000465 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Algorithmes. algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Algorithms fast Machine learning fast |
| topic_facet | Machine learning. Algorithms. Algorithms Machine Learning Apprentissage automatique. Algorithmes. algorithms. COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Machine learning Electronic book. |
| work_keys_str_mv | AT herbrichralf learningkernelclassifierstheoryandalgorithms |