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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Veröffentlicht in:Expert systems with applications Jg. 34; H. 3; S. 2013 - 2018
Hauptverfasser: Li, Der-Chiang, Fang, Yao-Hwei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.04.2008
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-2
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2007.02.016