A Convergent Nonlinear Smooth Support Vector Regression Model

作者：Tian Li ru^*; Zhang Xiao dan

来源：21st International Conference on Industrial Engineering and Engineering Management 2014 (IEEM), 2014-11-01 to 2014-11-02.

DOI：10.2991/978-94-6239-102-4_43

• Kernel
• nonlinear
• spline function
• support vector regression

Research on the non-smooth problems in the nonlinear support vector regression. A nonlinear smooth support vector regression model is proposed. Using a generalized cubic spline function approach the non-smooth part in the support vector regression model. The model of the nonlinear smooth support vector regression is solved by BFGS-Armijo. Then, the approximation accuracy and the astringency of the generalized cubic spline function to the epsilon - insensitive loss function were analyzed. As a result, we found the four-order and six times spline function's approximation effect is better than other smooth functions, and the nonlinear smooth support vector regression model, which be proposed in this paper is convergent.

• 出版日期2015
• 单位北京科技大学