Support vector machines

Alessia Mammone; Marco Turchi; Nello Cristianini

doi:10.1002/wics.49

journal article Nov 01, 2009

Support vector machines

Alessia Mammone Marco Turchi Nello Cristianini

WIREs Computational Statistics Vol. 1 No. 3 pp. 283-289 · Wiley

View at Publisher Save 10.1002/wics.49

Abstract

AbstractSupport vector machines (SVMs) are a family of machine learning methods, originally introduced for the problem of classification and later generalized to various other situations. They are based on principles of statistical learning theory and convex optimization, and are currently used in various domains of application, including bioinformatics, text categorization, and computer vision. Copyright © 2009 John Wiley & Sons, Inc.This article is categorized under:

Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification

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Citations

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References

Details

Published: Nov 01, 2009
Vol/Issue: 1(3)
Pages: 283-289
License: View

Authors

Department of Computer Science and Center for Molecular Biology of RNA, Department of Biology, University of California, Santa Cruz, Santa Cruz, CA 95064; Department of Computer Science, Columbia University, New York, NY 10025; Department of Engineering Mathematics, University of Bristol, Bristol BS8 1TR, United Kingdom

Cite This Article

Alessia Mammone, Marco Turchi, Nello Cristianini (2009). Support vector machines. WIREs Computational Statistics, 1(3), 283-289. https://doi.org/10.1002/wics.49

Support vector machines

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