Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Development of Fuzzy Support Vector Machine and Evaluation of Performance Using Ionosphere Radar Data

Fuzzy Twin Support Vector Machine 개발 및 전리층 레이더 데이터를 통한 성능 평가

  • 천민규 (연세대학교 전기전자공학부) ;
  • 윤창용 (연세대학교 전기전자공학부) ;
  • 김은태 (연세대학교 전기전자공학부) ;
  • 박민용 (연세대학교 전기전자공학부)
  • Published : 2008.08.25
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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.

Support Vector Machine(SVM)은 통계적 학습 이론에 기반을 둔 분류기이다. 또한 Twin Support Vector Machine(TWSVM)은 이진 SVM 분류기의 한 종류로써, 서로 관련된 두 개의 SVM 유형 문제를 통해 평행하지 않은 두개의 평면을 결정하고 이 두 평면을 통해 분류기를 완성하는 방식이다. 이러한 방식의 TWSVM은 학습 시간이 SVM에 비해 훨씬 짧으며, SVM과 비교하여 떨어지지 않는 성능을 보여준다. 본 논문은 분류기 입력에 Fuzzy Membership을 적용하는 방식의 TWSVM을 제안하고, 전리층 레이더 데이터를 이용한 실험을 통하여 기존에 세시 되었던 분류기와 비교한다.

Keywords

References

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