Improved Scheme for Fast Approximation to Least Squares Support Vector Regression  

作　　者：张宇宸 赵永平 宋成俊 侯宽新 脱金奎 叶小军 

机构地区：[1]School of Mechanical Engineering,Nanjing University of Science and Technology [2]Military Ammunition in Shenyang Representative Office [3]Civil Aviation Flight University of China [4]Heilongjiang North Tool Company Limited [5]New Star Research Institute of Applied Technology

出　　处：《Transactions of Nanjing University of Aeronautics and Astronautics》2014年第4期413-419,共7页南京航空航天大学学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China(51006052)

摘　　要：The solution of normal least squares support vector regression(LSSVR)is lack of sparseness,which limits the real-time and hampers the wide applications to a certain degree.To overcome this obstacle,a scheme,named I2FSA-LSSVR,is proposed.Compared with the previously approximate algorithms,it not only adopts the partial reduction strategy but considers the influence between the previously selected support vectors and the willselected support vector during the process of computing the supporting weights.As a result,I2FSA-LSSVR reduces the number of support vectors and enhances the real-time.To confirm the feasibility and effectiveness of the proposed algorithm,experiments on benchmark data sets are conducted,whose results support the presented I2FSA-LSSVR.The solution of normal least squares support vector regression（LSSVR）is lack of sparseness,which limits the real-time and hampers the wide applications to a certain degree.To overcome this obstacle,a scheme,named I2FSA-LSSVR,is proposed.Compared with the previously approximate algorithms,it not only adopts the partial reduction strategy but considers the influence between the previously selected support vectors and the willselected support vector during the process of computing the supporting weights.As a result,I2FSA-LSSVR reduces the number of support vectors and enhances the real-time.To confirm the feasibility and effectiveness of the proposed algorithm,experiments on benchmark data sets are conducted,whose results support the presented I2FSA-LSSVR.

关 键 词：support vector regression kernel method least squares SPARSENESS 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]