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《Acta Electronica Sinica》 2004-05
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Fuzzy Support Vector Machine for Multi-Class Classification

LI Kun-lun 1,2,HUANG Hou-kuan1,TIAN Sheng-feng1 (1 School of Computer & Information Technology,Beijing Jiaotong University,Beijing 100044,China; 2 School of Computer Science,Hebei University,Baoding,Hebei 071002,China)  
How to process multi-class problem with SVM is one of the present research focuses.We propose a fuzzy multi-class SVM model referred as FMSVM.It is constructed by introducing a fuzzy membership function to the penalty in the quadratic problem of Weston and Watkins,the membership function acquire different values for each input data according to their different affects on the classification results.Hence,we can ignore the data,which affect the classification result a little.Therefore different input points can make different contributions to the learning of the decision surface,i.e.,the optimal separating hyper-plane.Both theoretical analysis and digital experiment results show that the model proposed here works very well on benchmark data sets and also has the property of robustness.
【Fund】: 国家“十五”重点科技攻关项目 (No.2 0 0 2BA40 7B);; 河北省自然科学基金 (No .60 31 37)
【CateGory Index】: TP18
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