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

Der-Chiang Li, Yao Hwei Fang

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2013-2018
Number of pages6
JournalExpert Systems With Applications
Volume34
Issue number3
DOIs
Publication statusPublished - 2008 Apr 1

Fingerprint

Support vector machines
Clustering algorithms
Learning systems
Computational complexity
Experiments

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications

Cite this

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An algorithm to cluster data for efficient classification of support vector machines. / Li, Der-Chiang; Fang, Yao Hwei.

In: Expert Systems With Applications, Vol. 34, No. 3, 01.04.2008, p. 2013-2018.

Research output: Contribution to journalArticle

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