一种确定高斯核模型参数的新方法

支持向量机中核函数及其参数的选择非常重要，该文提出了一种利用支持向量之间的距离求取高斯核函数参数的有效方法。该方法充分利用了支持向量机方法的最优判别函数仅仅与支持向量有关，并且支持向量为高斯核中心的特点。实验结果表明，该方法较好地反映了图像特征的本质，解决了高斯核函数参数在实际使用中不易确定的问题。

A New Method for Determining the Parameter of Gaussian Kernel

The kernel and its parameters in support vector machine are important, an effect method for determining the parameter of Gaussian kernel based on the distances among the support vectors is proposed. The characters that the optimal discriminative function is determined by the support vectors, and the support vectors are centered as the Gaussian function, are considered in the method. Experimental results show that the method exhibits the essence of image feature space and solves a difficult problem for the parameter of Gaussian kernel in application.