Improved scheme to accelerate sparse least squares support vector regression

作     者：Yongping Zhao Jianguo Sun 

作者机构：ZNDY of Ministerial Key Lab Nanjing University of Science and Technology Nanjing 210094 E R. China College of Energy and Power Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 E R. China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2010年第21卷第2期

页      面：312-317页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 070801[理学-固体地球物理学] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(50576033) 

主　　题：least squares support vector regression machine pruning algorithm iterative methodology classification. 

摘      要：The pruning algorithms for sparse least squares support vector regression machine are common methods, and easily com- prehensible, but the computational burden in the training phase is heavy due to the retraining in performing the pruning process, which is not favorable for their applications. To this end, an im- proved scheme is proposed to accelerate sparse least squares support vector regression machine. A major advantage of this new scheme is based on the iterative methodology, which uses the previous training results instead of retraining, and its feasibility is strictly verified theoretically. Finally, experiments on bench- mark data sets corroborate a significant saving of the training time with the same number of support vectors and predictive accuracy compared with the original pruning algorithms, and this speedup scheme is also extended to classification problem.