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。
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。
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。
In this paper the boundary singular kernel method is used in EFGM to impose the essential boundary conditions exactly.
本文将边界奇异权方法运用于EFGM中，实现了本质边界条件在节点处的精确施加。
A new approach to numerical solution of the boundary-value problems of static-electric fields, the Modified Green Function kernel integral equation and its moment-method solution, is presented.
本文采用修正格林函数积分方程的矩量法数值解技术求解静电场边值问题。
A new approach to numerical solution of the boundary-value problems of static-electric fields, the Modified Green Function kernel integral equation and its moment-method solution, is presented.
本文采用修正格林函数积分方程的矩量法数值解技术求解静电场边值问题。
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