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.

Regularization, Optimization, Kernels, and Support Vector Machines

Regularization, Optimization, Kernels, and Support Vector Machines

Author: Johan A.K. Suykens

Publisher: CRC Press

Published: 2014-10-23

Total Pages: 528

ISBN-13: 1482241390

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Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso Discusses multi-layer SVMs Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing Describes graph-based regularization methods for single- and multi-task learning Considers regularized methods for dictionary learning and portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.


Book Synopsis Regularization, Optimization, Kernels, and Support Vector Machines by : Johan A.K. Suykens

Download or read book Regularization, Optimization, Kernels, and Support Vector Machines written by Johan A.K. Suykens and published by CRC Press. This book was released on 2014-10-23 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso Discusses multi-layer SVMs Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing Describes graph-based regularization methods for single- and multi-task learning Considers regularized methods for dictionary learning and portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.

Kernel Methods in Computational Biology

Kernel Methods in Computational Biology

Author: Bernhard Schölkopf

Publisher: MIT Press

Published: 2004

Total Pages: 428

ISBN-13: 9780262195096

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A detailed overview of current research in kernel methods and their application to computational biology.


Book Synopsis Kernel Methods in Computational Biology by : Bernhard Schölkopf

Download or read book Kernel Methods in Computational Biology written by Bernhard Schölkopf and published by MIT Press. This book was released on 2004 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed overview of current research in kernel methods and their application to computational biology.

Learning with Support Vector Machines

Learning with Support Vector Machines

Author: Colin Pigozzi

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 83

ISBN-13: 3031015525

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Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels


Book Synopsis Learning with Support Vector Machines by : Colin Pigozzi

Download or read book Learning with Support Vector Machines written by Colin Pigozzi and published by Springer Nature. This book was released on 2022-05-31 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels

Machine Learning Methods in the Environmental Sciences

Machine Learning Methods in the Environmental Sciences

Author: William W. Hsieh

Publisher: Cambridge University Press

Published: 2009-07-30

Total Pages: 364

ISBN-13: 0521791928

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A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.


Book Synopsis Machine Learning Methods in the Environmental Sciences by : William W. Hsieh

Download or read book Machine Learning Methods in the Environmental Sciences written by William W. Hsieh and published by Cambridge University Press. This book was released on 2009-07-30 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.

Kernel Methods for Pattern Analysis

Kernel Methods for Pattern Analysis

Author: John Shawe-Taylor

Publisher: Cambridge University Press

Published: 2004-06-28

Total Pages: 520

ISBN-13: 9780521813976

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Publisher Description


Book Synopsis Kernel Methods for Pattern Analysis by : John Shawe-Taylor

Download or read book Kernel Methods for Pattern Analysis written by John Shawe-Taylor and published by Cambridge University Press. This book was released on 2004-06-28 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher Description

Advanced Lectures on Machine Learning

Advanced Lectures on Machine Learning

Author: Shahar Mendelson

Publisher: Springer

Published: 2003-07-01

Total Pages: 267

ISBN-13: 354036434X

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Machine Learning has become a key enabling technology for many engineering applications and theoretical problems alike. To further discussions and to dis- minate new results, a Summer School was held on February 11–22, 2002 at the Australian National University. The current book contains a collection of the main talks held during those two weeks in February, presented as tutorial chapters on topics such as Boosting, Data Mining, Kernel Methods, Logic, Reinforcement Learning, and Statistical Learning Theory. The papers provide an in-depth overview of these exciting new areas, contain a large set of references, and thereby provide the interested reader with further information to start or to pursue his own research in these directions. Complementary to the book, a recorded video of the presentations during the Summer School can be obtained at http://mlg. anu. edu. au/summer2002 It is our hope that graduate students, lecturers, and researchers alike will ?nd this book useful in learning and teaching Machine Learning, thereby continuing the mission of the Summer School. Canberra, November 2002 Shahar Mendelson Alexander Smola Research School of Information Sciences and Engineering, The Australian National University Thanks and Acknowledgments We gratefully thank all the individuals and organizations responsible for the success of the workshop.


Book Synopsis Advanced Lectures on Machine Learning by : Shahar Mendelson

Download or read book Advanced Lectures on Machine Learning written by Shahar Mendelson and published by Springer. This book was released on 2003-07-01 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning has become a key enabling technology for many engineering applications and theoretical problems alike. To further discussions and to dis- minate new results, a Summer School was held on February 11–22, 2002 at the Australian National University. The current book contains a collection of the main talks held during those two weeks in February, presented as tutorial chapters on topics such as Boosting, Data Mining, Kernel Methods, Logic, Reinforcement Learning, and Statistical Learning Theory. The papers provide an in-depth overview of these exciting new areas, contain a large set of references, and thereby provide the interested reader with further information to start or to pursue his own research in these directions. Complementary to the book, a recorded video of the presentations during the Summer School can be obtained at http://mlg. anu. edu. au/summer2002 It is our hope that graduate students, lecturers, and researchers alike will ?nd this book useful in learning and teaching Machine Learning, thereby continuing the mission of the Summer School. Canberra, November 2002 Shahar Mendelson Alexander Smola Research School of Information Sciences and Engineering, The Australian National University Thanks and Acknowledgments We gratefully thank all the individuals and organizations responsible for the success of the workshop.

Pattern Recognition

Pattern Recognition

Author: Carl Edward Rasmussen

Publisher: Springer Science & Business Media

Published: 2004-08-23

Total Pages: 596

ISBN-13: 3540229450

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This book constitutes the refereed proceedings of the 26th Symposium of the German Association for Pattern Recognition, DAGM 2004, held in Tübingen, Germany in August/September 2004. The 22 revised papers and 48 revised poster papers presented were carefully reviewed and selected from 146 submissions. The papers are organized in topical sections on learning, Bayesian approaches, vision and faces, vision and motion, biologically motivated approaches, segmentation, object recognition, and object recognition and synthesis.


Book Synopsis Pattern Recognition by : Carl Edward Rasmussen

Download or read book Pattern Recognition written by Carl Edward Rasmussen and published by Springer Science & Business Media. This book was released on 2004-08-23 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 26th Symposium of the German Association for Pattern Recognition, DAGM 2004, held in Tübingen, Germany in August/September 2004. The 22 revised papers and 48 revised poster papers presented were carefully reviewed and selected from 146 submissions. The papers are organized in topical sections on learning, Bayesian approaches, vision and faces, vision and motion, biologically motivated approaches, segmentation, object recognition, and object recognition and synthesis.

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 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.