Rapid and brief communiction: Possibilistic support vector machines

Authors:
KiYoung Lee;Dae-Won Kim;Kwang H. Lee;Doheon Lee
Affiliations:
Department of Electrical Engineering & Computer Science, Korea Advanced Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea and Department of BioSyste ...;Department of BioSystems and Advanced Information Technology Research Center, Korea Advanced Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea;Department of Electrical Engineering & Computer Science, Korea Advanced Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea and Department of BioSyste ...;Department of BioSystems and Advanced Information Technology Research Center, Korea Advanced Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea
Venue:
Pattern Recognition
Year:
2005

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Abstract

We propose new support vector machines (SVMs) that incorporate the geometric distribution of an input data set by associating each data point with a possibilistic membership, which measures the relative strength of the self class membership. By using a possibilistic distance measure based on the possibilistic membership, we reformulate conventional SVMs in three ways. The proposed methods are shown to have better classification performance than conventional SVMs in various tests.