Learning Theory and Kernel Machines

Learning Theory and Kernel Machines

Author: Bernhard Schölkopf

Publisher: Springer

Published: 2003-11-11

Total Pages: 761

ISBN-13: 3540451676

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This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.


Book Synopsis Learning Theory and Kernel Machines by : Bernhard Schölkopf

Download or read book Learning Theory and Kernel Machines written by Bernhard Schölkopf and published by Springer. This book was released on 2003-11-11 with total page 761 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.

Learning with Kernels

Learning with Kernels

Author: Bernhard Scholkopf

Publisher: MIT Press

Published: 2018-06-05

Total Pages: 645

ISBN-13: 0262536579

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A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.


Book Synopsis Learning with Kernels by : Bernhard Scholkopf

Download or read book Learning with Kernels written by Bernhard Scholkopf and published by MIT Press. This book was released on 2018-06-05 with total page 645 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

Kernel Methods and Machine Learning

Kernel Methods and Machine Learning

Author: S. Y. Kung

Publisher: Cambridge University Press

Published: 2014-04-17

Total Pages: 617

ISBN-13: 1139867636

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Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.


Book Synopsis Kernel Methods and Machine Learning by : S. Y. Kung

Download or read book Kernel Methods and Machine Learning written by S. Y. Kung and published by Cambridge University Press. This book was released on 2014-04-17 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.

Learning Theory and Kernel Machines

Learning Theory and Kernel Machines

Author: Bernhard Schölkopf

Publisher: Springer

Published: 2003-11-11

Total Pages: 0

ISBN-13: 9783540451679

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Book Synopsis Learning Theory and Kernel Machines by : Bernhard Schölkopf

Download or read book Learning Theory and Kernel Machines written by Bernhard Schölkopf and published by Springer. This book was released on 2003-11-11 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Kernel Methods and Machine Learning

Kernel Methods and Machine Learning

Author: S. Y. Kung

Publisher: Cambridge University Press

Published: 2014-04-17

Total Pages: 617

ISBN-13: 110702496X

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Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science.


Book Synopsis Kernel Methods and Machine Learning by : S. Y. Kung

Download or read book Kernel Methods and Machine Learning written by S. Y. Kung and published by Cambridge University Press. This book was released on 2014-04-17 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science.

Learning Theory and Kernel Machines

Learning Theory and Kernel Machines

Author: Bernhard Schoelkopf

Publisher: Springer Science & Business Media

Published: 2003-08-11

Total Pages: 761

ISBN-13: 3540407200

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This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.


Book Synopsis Learning Theory and Kernel Machines by : Bernhard Schoelkopf

Download or read book Learning Theory and Kernel Machines written by Bernhard Schoelkopf and published by Springer Science & Business Media. This book was released on 2003-08-11 with total page 761 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.

Learning Theory and Kernel Machines

Learning Theory and Kernel Machines

Author:

Publisher:

Published: 2003

Total Pages:

ISBN-13:

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Book Synopsis Learning Theory and Kernel Machines by :

Download or read book Learning Theory and Kernel Machines written by and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Learning Kernel Classifiers

Learning Kernel Classifiers

Author: Ralf Herbrich

Publisher: MIT Press

Published: 2022-11-01

Total Pages: 393

ISBN-13: 0262546590

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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.


Book Synopsis Learning Kernel Classifiers by : Ralf Herbrich

Download or read book Learning Kernel Classifiers written by Ralf Herbrich and published by MIT Press. This book was released on 2022-11-01 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Advances in Kernel Methods

Advances in Kernel Methods

Author: Bernhard Schölkopf

Publisher: MIT Press

Published: 1999

Total Pages: 400

ISBN-13: 9780262194167

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A young girl hears the story of her great-great-great-great- grandfather and his brother who came to the United States to make a better life for themselves helping to build the transcontinental railroad.


Book Synopsis Advances in Kernel Methods by : Bernhard Schölkopf

Download or read book Advances in Kernel Methods written by Bernhard Schölkopf and published by MIT Press. This book was released on 1999 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: A young girl hears the story of her great-great-great-great- grandfather and his brother who came to the United States to make a better life for themselves helping to build the transcontinental railroad.

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Author: Nello Cristianini

Publisher: Cambridge University Press

Published: 2000-03-23

Total Pages: 216

ISBN-13: 9780521780193

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This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.


Book Synopsis An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by : Nello Cristianini

Download or read book An Introduction to Support Vector Machines and Other Kernel-based Learning Methods written by Nello Cristianini and published by Cambridge University Press. This book was released on 2000-03-23 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.