An algorithm to cluster data for efficient classification of support vector machines

Support vector machines (SVM) are widely applied to various classification problems. However, most SVM need lengthy computation time when faced with a large and complicated dataset. This research develops a clustering algorithm for efficient learning. The method mainly categorizes data into clusters...

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Vydáno v:	Expert systems with applications Ročník 34; číslo 3; s. 2013 - 2018
Hlavní autoři:	Li, Der-Chiang, Fang, Yao-Hwei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.04.2008
Témata:	Computational complexity Density-based clustering algorithm Machine learning Support vector machines Support vector machines Density-based clustering algorithm Computational complexity Machine learning
ISSN:	0957-4174, 1873-6793
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Support vector machines (SVM) are widely applied to various classification problems. However, most SVM need lengthy computation time when faced with a large and complicated dataset. This research develops a clustering algorithm for efficient learning. The method mainly categorizes data into clusters, and finds critical data in clusters as a substitute for the original data to reduce the computational complexity. The computational experiments presented in this paper show that the clustering algorithm significantly advances SVM learning efficiency.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0957-4174 1873-6793
DOI:	10.1016/j.eswa.2007.02.016