A Convergent Nonlinear Smooth Support Vector Regression Model

被引：1
|
作者：
Tian, Li-ru [1 ]
Zhang, Xiao-dan [1 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Math & Phys, Beijing 100083, Peoples R China
来源：
PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT 2014 | 2015年
关键词：
Kernel; nonlinear; spline function; support vector regression;
D O I：
10.2991/978-94-6239-102-4_43
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
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.
引用
收藏
页码：205 / 207
页数：3
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