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| Total Margin v-Support Vector Machine and Its Geometric Problem |
| PENG Xin-Jun1,2, WANG Yi-Fei3 |
1.Department of Computational Mathematics, Shanghai Normal University, Shanghai 200234
2.Scientific Computing Key Laboratory of Shanghai Universities, College of Mathematics and Science,Shanghai Normal University, Shanghai 200234
3.Department of Mathematics, Shanghai University, Shanghai 200444 |
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Abstract A total margin v-support vector machine (TM-v-SVM) is presented and it has better theoretical classification performance than v-SVM. The theoretical research shows that the TM-v-SVM is equivalent to the problem of finding the closest pair of points between two compressed convex hulls (CCHs) in the feature space. A geometric algorithm based on the theoretical properties of CCHs is proposed. Simulation results show that the TM-v-SVM and its geometric algorithm have better performance than the previous methods.
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Received: 03 December 2007
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2009, Vol. 22 
