Getting to grips with support vector machines: Theory

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dc.contributor.authorKroon S.
dc.contributor.authorOndin C.W.
dc.date.accessioned2011-05-15T16:02:31Z
dc.date.available2011-05-15T16:02:31Z
dc.date.issued2004
dc.description.abstractThe support vector machine (SVM) is a technique for function estimation which was proposed in the early 1990s. The technique provided state-of-the-art performance on many problems familiar to the machine learning community, and has hence gained enormous popularity among them. Despite the solid theoretical justification for the SVM as the solution of a regularization problem, the SVM has not yet become a common topic for study in statistics. This paper aims to provide an introductory overview of the SVM. A follow-up paper in this issue will provide useful hints and suggestions for newcomers to the field. The focus is on the SVM classifier, although the support vector technique is also applicable to regression and other techniques.
dc.description.versionArticle
dc.identifier.citationSouth African Statistical Journal
dc.identifier.citation38
dc.identifier.citation2
dc.identifier.issn0038271X
dc.identifier.urihttp://hdl.handle.net/10019.1/12509
dc.titleGetting to grips with support vector machines: Theory
dc.typeArticle
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