Fuzzy c-Regression Models Combined with Support Vector Regression

被引：0
|
作者：
Higuchi, Tatsuya [1 ]
Miyamoto, Sadaaki [2 ]
机构：
[1] Univ Tsukuba, Grad Sch Syst & Informat Engn, Tsukuba, Ibaraki 3058573, Japan
[2] Univ Tsukuba, Dept Risk Engn, Tsukuba, Ibaraki 3058573, Japan
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) | 2014年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Fuzzy c-regression models (FCRM) give us multiple clusters and regression models of each cluster simultaneously, while support vector regression models (SVRM) involve kernel methods which enable us to analyze non-linear structure of the data. We combine these two concepts and propose the united fuzzy c-support vector regression models (FC-SVRM). In case that c is unknown, we introduce sequential regression models (SRM) into SVRM, and propose support vector sequential regression models (SVSRM). We show numerical examples to compare results from these methods.
引用
收藏
页码：2489 / 2493
页数：5
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