Support Vector Machines

Xinhua Zhang

doi:10.1007/978-0-387-30164-8_804

Support Vector Machines

Xinhua Zhang

Research output: Chapter in Book/Report/Conference proceeding › Entry for encyclopedia/dictionary › peer-review

Abstract

Support vector machines (SVMs) are a class of linear algorithms that can be used for classification, regression, density estimation, novelty detection, and other applications. In the simplest case of two-class classification, SVMs find a hyperplane that separates the two classes of data with as wide a margin as possible. This leads to good generalization accuracy on unseen data, and supports specialized optimization methods that allow SVM to learn from a large amount of data.

Original language	English
Title of host publication	Encyclopedia of Machine Learning
Editors	Claude Sammut & Geoffrey I.Webb
Place of Publication	New York
Publisher	Springer
Pages	941-946pp
Volume	6
ISBN (Print)	9780387307688
DOIs	https://doi.org/10.1007/978-0-387-30164-8_804
Publication status	Published - 2010

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10.1007/978-0-387-30164-8_804

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title = "Support Vector Machines",

abstract = "Support vector machines (SVMs) are a class of linear algorithms that can be used for classification, regression, density estimation, novelty detection, and other applications. In the simplest case of two-class classification, SVMs find a hyperplane that separates the two classes of data with as wide a margin as possible. This leads to good generalization accuracy on unseen data, and supports specialized optimization methods that allow SVM to learn from a large amount of data.",

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AB - Support vector machines (SVMs) are a class of linear algorithms that can be used for classification, regression, density estimation, novelty detection, and other applications. In the simplest case of two-class classification, SVMs find a hyperplane that separates the two classes of data with as wide a margin as possible. This leads to good generalization accuracy on unseen data, and supports specialized optimization methods that allow SVM to learn from a large amount of data.

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DO - 10.1007/978-0-387-30164-8_804

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CY - New York

ER -

Support Vector Machines

Abstract

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