Journal of the Korean Data and Information Science Society
- 제15권2호
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- Pages.507-513
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- 2004
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- 1598-9402(pISSN)
Least-Squares Support Vector Machine for Regression Model with Crisp Inputs-Gaussian Fuzzy Output
- Hwang, Chang-Ha (Dept. of Statistical Information, Catholic University of Daegu)
- 발행 : 2004.05.31
초록
Least-squares support vector machine (LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. In this paper, we propose LS-SVM approach to evaluating fuzzy regression model with multiple crisp inputs and a Gaussian fuzzy output. The proposed algorithm here is model-free method in the sense that we do not need assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.