基于高斯核的SVM的参数选择

基于高斯核的支持向量机应用很广泛，高斯核参数σ的选择对分类器性能影响很大，本文提出了从核函数性质和几何距离角度来选择参数σ，并且利用高斯函数的麦克劳林展开解决了参数σ的优化选择问题。实验结果表明，该方法能较快地确定核函数参数σ，且 SVM 分类效果较好，解决了高斯核参数σ在实际应用中不易确定的问题。

Parameter Selection of SVM with Gaussian kernel

Support vector machine based on Gaussian kernel has been used in many areas. The parameter σ of the Gaussian kernel has great impact on the performance of the classifier. This paper proposes an approach to choose an optimal parameterσbased on the properties of the kernel function and the angle of geometric distance. What is more, we have solved the problem of the optimal option of the parameter σ by means of the McLaughlin expansion of the Gaussian kernel function. The experiment results indicate that this method can get parameter σ very quickly and can achieve high efficiency. Thus the difficulty of the estimation of the parameterσcan be solved by our method.