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。
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。
According to the statistic characteristic of covariance function, parameter estimation can be given for kernel function.
由协方差函数的统计特征，可给出核函数的参数估计。
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。
The key technology improves the system recognition rate is the SVM kernel function and parameter optimization.
该系统提高识别率的技术关键是SVM核函数的选取及其参数优化。
To serve this purpose, we use a grid search technique using 5-fold cross-validation to find out the optimal parameter values of various kernel function of SVM.
为了达到这个目标及保证可靠性，研究中使用网格5-折交叉确认来寻找不同核函数的最优参数。
To serve this purpose, we use a grid search technique using 5-fold cross-validation to find out the optimal parameter values of various kernel function of SVM.
为了达到这个目标及保证可靠性，研究中使用网格5-折交叉确认来寻找不同核函数的最优参数。
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