支持向量机中一种参数优化选取方法  

A Method of Optimal Cost Parameter Selection in Support Vector Machine

作　　者：董玉林[1] 夏尊铨[1] 杨慎恭[1] 

机构地区：[1]大连理工大学应用数学系,辽宁大连116024

出　　处：《运筹与管理》2007年第3期61-65,共5页Operations Research and Management Science

基　　金：国家自然科学基金资助项目(10471015)

摘　　要：本文给出一种支持向量机中的参数优化选取方法.它是通过遗传算法和确定性算法相结合解平衡约束优化问题,求出二分类支持向量机(SVM)中的正则参数C,本文将C作为优化问题中的变量来处理.遗传算法用来求解以C为变量的优化问题,而确定性算法对每一个C值求解约束.数值计算的结果表明,用文中所述的方法求得的C值能明显提高支持向量机的泛化性能.A method for selecting an optimal cost parameter C in support vector machine(SVM) is presented.This parameter is obtained by solving a mathematical program with equilibrium constraints(MPEC) via combining genetic algorithm and deterministic algorithm,but C is treated as a variable of optimization problem in this paper.Genetic algorithm is used to solve the optimization problem with respect to C,and deterministic algorithm is used to obtain the constraints of problem.Numerical results show that the generalization performance of the support vector machine can be improved clearly by the proposed method in this paper.

关 键 词：支持向量机 正则参数 遗传算法 平衡约束规划 

分 类 号：O221[理学—运筹学与控制论] TP181[理学—数学]