Using membership functions to improve multi-class SVM classification

被引：0
|
作者：
Wang, XD [1 ]
Wu, CM [1 ]
机构：
[1] AF Engn Univ, Missile Inst, San Yuan 713800, Shaanxi, Peoples R China
来源：
2004 7TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, VOLS 1-3 | 2004年
关键词：
support vector machine; multi-class classification;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Support vector machine is originally designed for binary classification. One-agamst-one method is commonly used in multi-class classification. Based on the analysis of the decision process of max wins used in one-against-one method, new membership functions are introduced to resolve the possibly existed unclassifiable regions, and a fuzzy SVM multi-class classification algorithm FSVM is proposed. Classification experiments result proves the effectiveness of FSVM.
引用
收藏
页码：1459 / 1462
页数：4
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