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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2004.6
- 수록면
- 449 - 455 (7page)
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초록· 키워드
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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.
목차
등록된 정보가 없습니다.
참고문헌 (5)
참고문헌 신청
[학술지(정기간행물)] - / 2003 / Support vector fuzzy regression machines 138 : 271 ~ 281
[학술지(정기간행물)] - / 2003 / Support vector interval regression networks for interval regression analysis 148 : 283 ~ 300
[학술지(정기간행물)] - / 1992 / Linear regression analysis with fuzzy model : 903 ~ 907
[학술지(정기간행물)] - / 1998 / Interval regression analysis by quadratic programming approach 6 : 473 ~ 481
[학술지(정기간행물)] - / 1995 / The Nature of Statistical Learning Theory
[학술지(정기간행물)] - / 2003 / Support vector interval regression networks for interval regression analysis 148 : 283 ~ 300
[학술지(정기간행물)] - / 1992 / Linear regression analysis with fuzzy model : 903 ~ 907
[학술지(정기간행물)] - / 1998 / Interval regression analysis by quadratic programming approach 6 : 473 ~ 481
[학술지(정기간행물)] - / 1995 / The Nature of Statistical Learning Theory
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UCI(KEPA) : I410-ECN-0101-2018-041-001392212