Kernel parameter of the SVC algorithm plays an important role in clustering formation, which affects the boundary and shape of cluster.

SVC算法中的核函数参数对聚类的形成起着决定性的作用，并影响着聚类的边界和形状。

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By choosing suitable boundary normalized equation in according to R-function theory, the irregularity of integral kernel in integral equation can be eliminated.

得到的积分方程中的积分核具有奇异性，再根据R -函数理论，可以选择适当的边界规范化方程，消除核的奇异性。

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The kernel based weighted KNN algorithm solves the multi peak distribution problem and the overlap boundary problem of the sample set, as well as the classifier's precise decision problem.

基于核的距离加权KNN算法解决了样本的多峰分布、边界重叠问题和分类器的精确分类决策问题。

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