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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2002.12
- 수록면
- 65 - 75 (11page)
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The support vector machine(SVM), first developed by Vapnik and his group at AT&T Bell Laboratories, is being used as a new technique for regression and classification problems. In this paper we present an approach to estimating approximate prediction intervals for SVM regression based on posterior predictive densities. Furthermore, the method is illustrated with a data example.
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UCI(KEPA) : I410-ECN-0101-2018-041-001390410