Abstract: Searching the optimal parameters is one of the most important area of SVM and often named as parameter optimization or parameter selection. The L2-SVM can convert the samples into linearly separable problem. Based on the performance, this paper proposes the L2-SVM with two regularization parameters, and the dual formulation of L2-SVM with two regularization  parameters is deduced. Combining the objective function established on minimizing the VC dimension and the gradient method, a new algorithm called Doupenalty-Gradient is present. Ten benchmark datasets are used in the experiments, and the classifying accuracy is improved obviously. The experimental results show the wonderful property and the feasibility of Doupenalty-Gradient.

Key words: statistical learning theory, support vector machines, VC dimension, parameter selection

摘要： 摘  要：寻找支持向量机（SVM）的最优参数是支持向量机研究领域的热点之一。2范数软间隔SVM（L2-SVM）将样本转化成线性可分，在原始单正则化参数L2-SVM的基础上，提出双正则化参数的L2-SVM，获得它的对偶形式，从而确定了最优化的目标函数。然后结合梯度法，提出了一种新的支持向量机参数选择的新方法（Doupenalty-Gradient）。实验使用了10个基准数据集，结果表明，Doupenalty-Gradient方法是可行且有效的。对于实验所用的样本，极大地改善了分类精度。

关键词: 统计学习理论, 支持向量机, VC维, 参数选择