LINEAR PROGRAMMING REGRESSIVE SUPPORT VECTOR MACHINE
Based on the analysis of the general norm in structure risk to control model complexity for regressive problem, two kinds of linear programming support vector machine corresponding to l1-norm and l∞-norm are presented including linear and nonlinear SVMs. A numerical experiment has been done for these two kinds of linear programming support vector machines and classic support vector machine by artificial data. Simulation results show that the generalization performance of this two kind linear programming SVM is similar to classic one, l1-SVM has less number of support vectors and faster learning speed, and learning result is not sensitive to learning parameters.
Statistical learning theory VC dimension Support vector machines Linear programming
HONG XIE JIANG-PING WEI HE-LI LIU
Dept of Electronic Engineering, Shanghai Maritime University, Shanghai, 200135, China Dept of Computer Engineering, Jiangsu College of Information Technology, Wuxi, 214061, China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
2196-2199
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）