Fuzzy support vector machines based on fuzzy similarity degree

被引：0
|
作者：
Wu, ZD [1 ]
Xie, WX [1 ]
Yu, JP [1 ]
机构：
[1] Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China
来源：
ISTM/2003: 5TH INTERNATIONAL SYMPOSIUM ON TEST AND MEASUREMENT, VOLS 1-6, CONFERENCE PROCEEDINGS | 2003年
关键词：
D O I：
暂无
中图分类号：
TH7 [仪器、仪表];
学科分类号：
0804 ; 080401 ; 081102 ;
摘要：
Support Vector Machines (SVM) for classification problem with fuzzy inputs is proposed. This is based on Mercer kernels, or equivalently, positive definite kernel matrix. We use fuzzy similarity degree as similarity (dissimilarity) between two fuzzy vectors, and construct positive definite kernel matrix that is based on fuzzy similarity degree. We call this novel SVM as Fuzzy Support Vector Machines (FSVM). The merit of FSVM is that it can incorporate with domain knowledge represented by fuzzy IF-THEN rules to improvement of performance of the conventional SVM in incomplete numeral data set for training. The simulation results are very encouraging.
引用
收藏
页码：1187 / 1190
页数：4
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