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。

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.

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核函数是SVM的关键技术，核函数的选择将影响着支持向量机的学习能力和泛化能力。

Kernel function is the key technology of SVM, the choice of kernel will affect the learning ability and generalization ability of SVM.

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通过线性规划技术和采用尺度函数作为核函数来实现支持向量回归模型。

Using linear programming technique and scaling kernel function, the support vector regression model was obtained.

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