Journal of the Korean Data and Information Science Society
- 제15권2호
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- Pages.449-455
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- 2004
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- 1598-9402(pISSN)
Quadratic Loss Support Vector Interval Regression Machine for Crisp Input-Output Data
- Hwang, Chang-Ha (Dept. of Statistical Information, Catholic University of Daegu)
- 발행 : 2004.05.31
초록
Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval regression models for crisp input-output data. The proposed method is based on quadratic loss SVM, which implements quadratic programming approach giving more diverse spread coefficients than a linear programming one. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.