在高斯核支持向量机 ( SVM) 的参数优化中，针对以特征空间中的类间距离( ICDF) 为测度选择核参数时存在计算量大、耗时长的问题，首先提出并证明了 ICDF 是
高斯核参数的严格单峰正定函数，然后根据该结论提出了改进黄金分割法( MGSA) 来快速搜索核参数在候选集中的最佳值，在此基础上提出一种基于 MGSA 和微分进化算法的SVM 参数快速优化方法，最后通过比较实验验证了所提方法的有效性和快速性．

In the process of parameter optimization for support vector machines ( SVMs) with Gaussian kernel,in- ter-cluster distance in feature spaces ( ICDF) is an effective measure.However,ICDF may result in heavy compu- tational load and large time consumption.In order to solve this problem,firstly,the theorem that ICDF is a positive strictly-unimodal function about Gaussian kernel parameter is proved.Then,according to this theorem,a modified golden section algorithm ( MGSA) is proposed to search a shrunk value fast for kernel parameter in the candidate set.Thus,a fast parameter optimization approach on the basis of both MGSA and differential evolutionary algorithm is presented.Finally,some experiments are carried out to verify the effectiveness and rapidity of the proposed ap- proach.