To apply the discriminative classifier in the speaker recognition, the building sequence kernel support vector machine(SVM) becomes the trend in the field.

为了更好地将区分式分类方法应用于说话者确认系统中，构建序列核支持向量机已成为说话人识别领域的研究热点与趋势。

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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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For this reason, several important properties of kernel function are discussed on the basis of support vector machine.

为此，在研究支持向量机的基础上，给出了核函数的若干重要性质。

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