中文摘要：

支持向量机（SVM）的性能与SVM参数的选择有关．SVM参数的优化需要一个准则，本文提出了一种以原空间中样本到分类面的最短代数距离最大为准则的SVM参数优化方法．该方法旨在使SVM分类面在原空间中使样洙“平分秋色”，更能体现SVM分类器的结构风险最小化的原则．算法简单、几何直观性好、易于实现，通过在双螺旋线样本和Iris样本来上所作测试证明了该方法的有效性。

英文摘要：

The performance of Support Vector Machine （SVM） is determined by its hyper-parameters, optimizing the hyper- parameters needs a criterion. This paper presents a new SVM hyper-parameters optimization method, in which maximizing the minimum algebraic distance from samples to the class-separating hyper-surface in input space is taken as the criterion. The main purpose of this method is to＇ leg and leg＇ the whole original input space for all the samples, and it sustains the structural risk minimization principle better. The method is simple, geometric intuitive and can be implemented easily; The feasibility of the method is displayed through experiments on two classical benchmark classification problems-Two Spirals Problem （TSP） and Iris samples.