한국데이터정보과학회:학술대회논문집
- 2004.10a
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- Pages.53-59
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- 2004
Estimating Fuzzy Regression with Crisp Input-Output Using Quadratic Loss Support Vector Machine
- Hwang, Chang-Ha (Dept. of Statistical Information, Catholic University of Daegu) ;
- Hong, Dug-Hun (Dept. of Mathematics, Myongji University) ;
- Lee, Sang-Bock (Dept. of Statistical Information, Catholic University of Daegu)
- Published : 2004.10.30
Abstract
Support vector machine(SVM) approach to regression can be found in information science literature. SVM implements the regularization technique which has been introduced as a way of controlling the smoothness properties of regression function. In this paper, we propose a new estimation method based on quadratic loss SVM for a linear fuzzy regression model of Tanaka's, and furthermore propose a estimation method for nonlinear fuzzy regression. This approach is a very attractive approach to evaluate nonlinear fuzzy model with crisp input and output data.
Keywords
- Crisp data;
- fuzzy regression;
- linear programming;
- quadratic loss;
- quadratic programming;
- support vector machine