-- Kernel Support Vector Machine [核型支持向量机] -- Soft-Margin Support Vector Machine [软式支持向量机] .
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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。
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。
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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