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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2004.6
- 수록면
- 507 - 513 (7page)
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초록· 키워드
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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.
목차
등록된 정보가 없습니다.
참고문헌 (10)
참고문헌 신청
[학술지(정기간행물)] - Albrecht,M / 1992 / Approximation of functional relationships to fuzzy observations 49 : 301 ~ 305
[학술지(정기간행물)] - Diamond,P / 1988 / Fuzzy least squares 46 : 141 ~ 157
[학술지(정기간행물)] - Gunn S / 1998 / Support vector machines for classification and regression
[학술지(정기간행물)] - Hong D / 2003 / Support vector fuzzy regression machines 138 : 271 ~ 281
[학술지(정기간행물)] - Hong D / 2004 / Ridge estimation for regression models with crisp inputs and Gaussian fuzzy output 142 : 307 ~ 319
[학술지(정기간행물)] - Diamond,P / 1988 / Fuzzy least squares 46 : 141 ~ 157
[학술지(정기간행물)] - Gunn S / 1998 / Support vector machines for classification and regression
[학술지(정기간행물)] - Hong D / 2003 / Support vector fuzzy regression machines 138 : 271 ~ 281
[학술지(정기간행물)] - Hong D / 2004 / Ridge estimation for regression models with crisp inputs and Gaussian fuzzy output 142 : 307 ~ 319
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UCI(KEPA) : I410-ECN-0101-2018-041-001392287