中文摘要：

提出一种选择支持向量分类（Support Vector Classification，SVC）最优核参数的算法，称为MI（Maximum Isolation）算法，通过定义样本间的独立性，可以获得最优核参数和相应的最优学习模型。该算法可以在支持向量机训练之前得到最优的核参数，计算代价较小，实验证明所提出的算法简单有效。

英文摘要：

This paper presents an algorithm of choosing optimal kernel parameters for support vector classification, namely MI algorithm. By defining the data isolation among samples, the optimal kernel parameters and the optimal learning model can be obtained. Because the optimal kernel parameters can be obtained before SVM training, the less computation cost is needed. Simulation results demonstrate the simpleness and validity of the presented approach.