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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

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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.

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Series Title

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Call Number

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Publisher

Machine learning.;Algorithms.; : Cambridge, Mass. : MIT Press,., 2002

Collation

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Language

English

ISBN/ISSN

9780262256933

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

1

Subject(s)

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Specific Detail Info

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Statement of Responsibility

Sch{uml}olkopf, Bernhard.

Other Information

Cataloger

Suwardi

Source

https://direct.mit.edu/books/book/1821/Learning-with-KernelsSupport-Vector-Machines

Validator

Suwardi

Digital Object Identifier (DOI)

https://doi.org/10.7551/mitpress/4175.001.0001

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