线性规划支撑矢量机

Linear Programming Support Vector Machines

本文通过对统计学习理论中一些重要结论 ,特别是线性函数VC维数的分析 ,得到了一种线性规划支撑矢量机 ,包括线性规划线性支撑矢量机和线性规划非线性支撑矢量机 .在线性规划支撑矢量机中 ,对其VC维数界作了适当的放宽 .文中最后对人工和实际样本进行了实验 ,结果说明了线性规划支撑矢量机在推广能力上较好地逼近了原支撑矢量机 ,而在计算复杂度上明显低于原支撑矢量机 .

Based on analysis of the conclusions in the statistical learning theory, especially the VC dimension of linear functions, linear programming SVMs are presented, including linear programming linear and nonlinear SVMs. In linear programming linear SVMs, the bound of the VC dimension is loosened properly. Simulation results for both artificial and real data show the generalization performance of our method is a good approximation of SVMs and the computation complexity is largely reduced by our method.