일반화 서포트벡터 분위수회귀에 대한 연구
Generalized Support Vector Quantile Regression
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다운로드
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참고문헌
키워드
초록
Support vector regression (SVR) is devised to solve the regression problem by utilizing the excellent predictive power of Support Vector Machine. In particular, the -insensitive loss function, which is a loss function often used in SVR, is a function thatdoes not generate penalties if the difference between the actual value and the estimated regression curve is within . In most studies, the -insensitive loss function is used symmetrically, and it is of interest to determine the value of . In SVQR (Support Vector Quantile Regression), the asymmetry of the width of and the slope of the penalty was controlled using the parameter p. However, the slope of the penalty is fixed according to the p value that determines the asymmetry of . In this study, a new ε-insensitive loss function with and parameters was proposed. A new asymmetric SVR called GSVQR (Generalized Support Vector Quantile Regression) based on the new ε-insensitive loss function can control the asymmetry of the width of and the slope of the penalty using the parameters and , respectively. Moreover, the figures show that the asymmetry of the width of and the slope of the penalty is controlled. Finally, through an experiment on a function, the accuracy of the existing symmetric Soft Margin, asymmetric SVQR, and asymmetric GSVQR was examined, and the characteristics of each were shown through figures.
Abstract
Support vector regression (SVR) is devised to solve the regression problem by utilizing the excellent predictive power of Support Vector Machine. In particular, the -insensitive loss function, which is a loss function often used in SVR, is a function thatdoes not generate penalties if the difference between the actual value and the estimated regression curve is within . In most studies, the -insensitive loss function is used symmetrically, and it is of interest to determine the value of . In SVQR (Support Vector Quantile Regression), the asymmetry of the width of and the slope of the penalty was controlled using the parameter p. However, the slope of the penalty is fixed according to the p value that determines the asymmetry of . In this study, a new ε-insensitive loss function with and parameters was proposed. A new asymmetric SVR called GSVQR (Generalized Support Vector Quantile Regression) based on the new ε-insensitive loss function can control the asymmetry of the width of and the slope of the penalty using the parameters and , respectively. Moreover, the figures show that the asymmetry of the width of and the slope of the penalty is controlled. Finally, through an experiment on a function, the accuracy of the existing symmetric Soft Margin, asymmetric SVQR, and asymmetric GSVQR was examined, and the characteristics of each were shown through figures.
참고문헌 (12)
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