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.
通过相隔固定的帧差值阅值化得到背景样本值，并采用高斯核密度估计方法计算背景灰度的概率密度函数。
The background samples are chosen by thresholding inter-frame differences, and the Gaussian kernel density estimation is used to estimate the probability density function of background intensity.
该方法采用核密度估计模型来构造近似密度函数，利用爬山策略来提取聚类模式。
This method USES kernel density estimation model to construct the approximate density function, and takes hill climbing strategy to extract clustering patterns.
通过高斯分布函数描述形核质点密度随温度的分布关系，在给定过冷度时对分布函数求积分可得该时刻的形核密度。
Gauss distribution function was employed to describe the relation between the density of nucleation sites and the temperature and was integrated to get the grain density at a given undercooling.
推广后的定位方法，可根据具体的目标定位场合，灵活选择核函数对样本点进行核密度估计。
Using this method, kernel function could be flexibly chosen to estimate sample point's density values according to different locating application scenes.
本文基于非参数核密度估计与核回归估计的基础上，介绍了合理选取核函数及窗宽的原则和方法。
This paper introduced the selection principle and method about a reasonable kernel function and bandwidth based on the nonparametric kernel density estimation and kernel regression estimation.
多元统计过程介绍了三种主要的方法：主元分析法、偏最小二乘法和核函数概率密度估计法。
About multivariate statistical process, three methods are introduced: Principal Component Analysis, Partial Least Squares, Kernel Density Estimation.
多元统计过程介绍了三种主要的方法：主元分析法、偏最小二乘法和核函数概率密度估计法。
About multivariate statistical process, three methods are introduced: Principal Component Analysis, Partial Least Squares, Kernel Density Estimation.
应用推荐