A Novel Smooth Support Vector Regression based on CHKS Function

摘要：

　　This paper presents a new smooth approach to solve support vector regression (SVR).Based on Karush-Kuhn-Tucker complementary condition in optimization theory,a smooth unconstrained optimization model for SVR is built.Since the objective function of the unconstrained SVR model is non-smooth,we apply the smooth techniques and replace the εinsensitive loss function by CHKS function.Newton-Armijo algorithm is used to solve the smooth CHKS-SSVR model.Primary numerical results illustrate that our proposed approach improves the regression performance and the learning efficiency.

关键词： optimization theory smooth approximation support vector regression CHKS function Newton-Armijo algorithm

作者: Qing Wu

作者单位: School of Automation, Xian Institute of Posts and Telecommunication,Xian, Shaanxi, 710121 P.R.China

会议类型: 国际会议

会议名称: the 2010 International Conference on Frontiers of Manufacturing and Design Science(第一届制造与设计科学国际会议(ICFMD 2010))

会议地点: 重庆

会议语种:英文

页码: 3746-3751

在线出版日期: 2010-12-11（万方平台首次上网日期，不代表论文的发表时间）