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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) - Hardcover
Synopsis
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.
"synopsis" may belong to another edition of this title.
About the Author
Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.
Review
Interesting and original. Learning with Kernels will make a fine textbook on this subject.
―Grace Wahba, Bascom Professor of Statistics, University of Wisconsin MadisonThis splendid book fills the need for a comprehensive treatment of kernel methods and support vector machines. It collects results, theorems, and discussions from disparate sources into one very accessible exposition. I am particularly impressed that the authors have included problem sets at the end of each chapter; such problems are not easy to construct, but add significantly to the value of the book for the student audience.
―Chris J. C. Burges, Microsoft Research"About this title" may belong to another edition of this title.
- PublisherMit Pr
- Publication date2001
- ISBN 10 0262194759
- ISBN 13 9780262194754
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages644
- Rating
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)
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Mit Pr (edition First Edition), 2001
ISBN 10: 0262194759
ISBN 13: 9780262194754
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