基于Fisher准则的SVM参数选择算法

A SVM parameters selection algorithm based on Fisher criterion

支持向量机(support vector machine,SVM)分类性能主要受到SVM模型选择(包括核函数的选择和参数的选取)的影响,目前SVM模型参数选择的方法并不能较好地确定模型参数。为此基于Fisher准则提出了SVM参数选择算法。该算法利用样本在特征空间中的类别间的线性可分离性,结合梯度下降算法进行参数寻优,并基于Matlab实现选择算法。实验结果表明参数选择算法既提高了SVM训练性能,又大大减少了训练时间。

SVM （support vector machine） classification performance is mainly influenced by the SVM model selection （ including the choice of the kernel function and parameters selected）. It is not better to determine the SVM model pa- rameters by the existing methods of SVM model parameter selection. Therefore a SVM parameter selection algorithm is presented based on the Fisher criterion. The selection algorithm makes full use of the samples of linear separability in the classes in the feature space, and combines with the gradient descent algorithm for parameter optimization. It is realized by Matlab. The experimental results show that this parameter selection algorithm not only improves the training performance of SVM, but also greatly reduces the training time through the simulation.