利用核密度函数 Kernel Density Distribution
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讨论了在适当条件下，密度函数核估计的一致强相合性。
Under certain conditions, We discuss the uniform strong consistency of kernal estimator for the density function.
并用正态核加权和密度函数拟合法解决了非正态图象模板匹配的优化问题。
By means of the density function fitting method of weighted sum of normal kernels the optimizing problem of template matching for non-normal distribution image is solved.
本文提出了利用一维核函数构造多维密度函数一个新估计的方法。
In this paper, a new kernel estimator of multivariate density is proposed by using a univariate kernel function.
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