일반화 서포트벡터 분위수회귀에 대한 연구

• 영문명: Generalized Support Vector Quantile Regression
• 발행기관: 한국산업경영시스템학회
• 저자명: 이동주(Dongju Lee) 최수진(Sujin Choi)
• 간행물 정보: 『산업경영시스템학회지』제43권 제4호, 107~115쪽, 전체 9쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2020.12.30

영문 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 ε-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 ε. In most studies, the ε-insensitive loss function is used symmetrically, and it is of interest to determine the value of ε. 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 ε. In this study, a new ε-insensitive loss function with p₁ and p₂ 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 ε and the slope of the penalty using the parameters p₁ and p₂, respectively. Moreover, the figures show that the asymmetry of the width of ε 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.

목차

1. 서 론

2. 기존의 ε-둔감함수와 수학모형

3. 제안하는 General Support Vector Quantile Regression Model(GSQVR)

4. 실 험

5. 결 론

References

키워드

Epsilon Insensitive Loss Function Machine Learning Asymmetric Support Vector Regression