Fuzzy probability c-regression estimation based on least squares support vector machine

Authors:
Zonghai Sun
Affiliations:
College of Automation Science and Engineering, South China University of Technology, Guangzhou, China
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

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Abstract

The problem of regression estimation for large data set is viewed as a problem of multiple models estimation. In this paper the method of fuzzy probability c-regression based on least squares support vector machine is proposed to classify the multiple models while estimating these models. The algorithm for solving it is also provided. The numerical example is used to illustrate that our approach can be used to fit nonlinear models for mixed data set. The simulation results demonstrate that the method of fuzzy probability c-regression based on least squares support vector machine can discriminate the multiple regression models with a fuzzy partition of data set while fitting perfectly these models and can overcome the problem of initialization that often makes termination occurring at local minima.