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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제6권 제2호
- 발행연도
- 2004.1
- 수록면
- 431 - 439 (9page)
이용수
초록· 키워드
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Support vector machine(SVM) has been very successful in pattern recognition and function estimation problems for crisp data. In this paper, we propose 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. This algorithm is robust for the estimation of fuzzy linear and nonlinear regression models, especially when outliers are present. Numerical examples are given to detail the effectiveness of this approach.
목차
등록된 정보가 없습니다.
참고문헌 (9)
참고문헌 신청
/ 1982 / Linear regression analysis with fuzzy model : 903 ~ 907
/ 1988 / Fuzzy least squares 46 : 141 ~ 157
/ 1991 / A linear regression model in fuzzy environment : 31 ~ 40
/ 1995 / The Nature of Statistical Learning Theory
/ 1996 / On a class of fuzzy c-numbers clustering procedures for fuzzy data 84 : 49 ~ 60
/ 1988 / Fuzzy least squares 46 : 141 ~ 157
/ 1991 / A linear regression model in fuzzy environment : 31 ~ 40
/ 1995 / The Nature of Statistical Learning Theory
/ 1996 / On a class of fuzzy c-numbers clustering procedures for fuzzy data 84 : 49 ~ 60
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