Fuzzy c-regression models combined with support vector regression

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.

[1]  Sadaaki Miyamoto,et al.  Semi-supervised Sequential Kernel Regression Models with Pairwise Constraints , 2013, MDAI.

[2]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[3]  Rajesh N. Davé,et al.  Robust clustering methods: a unified view , 1997, IEEE Trans. Fuzzy Syst..

[4]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[5]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[6]  R.J. Hathaway,et al.  Switching regression models and fuzzy clustering , 1993, IEEE Trans. Fuzzy Syst..

[8]  Zonghai Sun.  Fuzzy Probability C-Regression Estimation Based on Least Squares Support Vector Machine , 2006, ICONIP.

[9]  Rajesh N. Davé,et al.  Characterization and detection of noise in clustering , 1991, Pattern Recognit. Lett..

[10]  Sadaaki Miyamoto,et al.  Algorithms for Fuzzy Clustering - Methods in c-Means Clustering with Applications , 2008, Studies in Fuzziness and Soft Computing.

[11]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.