The selection of the kernel function parameter and error penalty factor affected the precision of the support vector machine (SVM) significantly.

核函数参数和误差惩罚因子的选择对支持向量机模型（SVM）的精度有较大影响。

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In the paper, GBGM-GA is seen the optimization technique combining KPCA and GA, and is suitable to the optimization selection of kernel function parameter.

本文采用高斯变异遗传算法作优化技术，实现了KPCA和GA的集成，适合核函数参数的优化选择。

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For SVM, in this paper, a kernel function selection and parameter adjustment algorithm are presented. It can get optimal parameter adjustment in a given training set.

对于SVM，本文给出了一个核函数选择与参数调整的算法，它能够对给定训练集得到最优的参数调整。

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