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Possibilistic support vector machines |
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| Authors: | KiYoung Lee Dae-Won Kim Doheon Lee |
| Affiliation: | a Department of Electrical Engineering & Computer Science, Korea Advanced Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea b 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 |
| 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. |
| Keywords: | Classification Support vector machines Possibilistic SVMs Geometric distribution Possibilistic distance |
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