Clifford Fuzzy Support Vector Machines for Classification

被引：0
|
作者：
Rui Wang
Xiaoyan Zhang
Wenming Cao
机构：
[1] Shanghai University,School of Communication and Information Engineering
[2] Shenzhen University,Shenzhen Key Laboratory of Media Security
来源：
Advances in Applied Clifford Algebras | 2016年 / 26卷
关键词：
Classification; Clifford geometric algebra; Clifford SVM; Clifford fuzzy SVM; Support vector machines (SVM); Fuzzy membership;
D O I：
暂无
中图分类号：
学科分类号：
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.
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页码：825 / 846
页数：21
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