Fuzzy Support Vector Machine for Multi-Class Classification
LI Kun-lun1,2, 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
Abstract：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.
李昆仑;黄厚宽;田盛丰. 模糊多类SVM模型[J]. 电子学报, 2004, 32(5): 830-832.
LI Kun-lun;HUANG Hou-kuan;TIAN Sheng-feng. Fuzzy Support Vector Machine for Multi-Class Classification. Chinese Journal of Electronics, 2004, 32(5): 830-832.
2004, Vol. 32 
