Clifford Fuzzy Support Vector Machines for Classification

被引：13

作者：

Wang, Rui ^{[1
]}

Zhang, Xiaoyan ^{[1
]}

Cao, Wenming ^{[2
]}

机构：

[1] Shanghai Univ, Sch Commun & Informat Engn, Shanghai, Peoples R China

[2] Shenzhen Univ, Shenzhen Key Lab Media Secur, Shenzhen, Guangdong, Peoples R China

来源：

ADVANCES IN APPLIED CLIFFORD ALGEBRAS | 2016年 / 26卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Classification; Clifford geometric algebra; Clifford SVM; Clifford fuzzy SVM; Support vector machines (SVM); Fuzzy membership;

D O I：

10.1007/s00006-015-0616-z

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

A Clifford support vector machine (CSVM) learns the decision surface from multi distinct classes of the multiple input points using the Clifford geometric algebra. In many applications, each multiple input point may not be fully assigned to one of these multi-classes. In this paper, we apply a fuzzy membership to each multiple input point and reformulate the CSVM for multiclass classification to make different input points have their own different contributions to the learning of decision surface. We call the proposed method Clifford fuzzy SVM.

引用

页码：825 / 846

页数：22

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