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http://dx.doi.org/10.5391/JKIIS.2008.18.4.549

Development of Fuzzy Support Vector Machine and Evaluation of Performance Using Ionosphere Radar Data  

Cheon, Min-Kyu (연세대학교 전기전자공학부)
Yoon, Chang-Yong (연세대학교 전기전자공학부)
Kim, Eun-Tai (연세대학교 전기전자공학부)
Park, Mig-Non (연세대학교 전기전자공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.18, no.4, 2008 , pp. 549-554 More about this Journal
Abstract
Support Vector machine is the classifier which is based on the statistical training theory. Twin Support Vector Machine(TWSVM) is a kind of binary classifier that determines two nonparallel planes by solving two related SVM-type problems. The training time of TWSVM is shorter than that of SVM, but TWSVM doesn't shows worse performance than that of SVM. This paper proposes the TWSVM which is applied fuzzy membership, and compares the performance of this classifier with the other classifiers using Ionosphere radar data set.
Keywords
pattern classification; Support Vector Machine; Twin Support Vector Machine; Fuzzy Membership; Ionosphere radar data;
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