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一种核密度估计,或者称作带宽选择的方法,可以估计二维尺度参数,至于多维以上的估计方法尚在开发,多维情况下个人经验好的方法是多次实验取较好值,kernel density estimation, bandwidth selection, two-dimensional scale parameter can be estimated ,for the multi-dimensional approaches are still under development, multi-dimensiona
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以为高斯和密度估计,使用高斯核的非参数密度估计方法,对样本进行概率密度估计,程序中给出了窗宽的估算公式。-That the Gaussian and density estimation, using Gaussian kernel non-parametric density estimation method, the sample probability density estimates, the program gives the formula for bandwidth estim
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利用正态分布和核密度估计计算分位数。包括正态分布分位数函数、核估计概率密度函数、核估计累计分布概率函数、核估计计算分位数函数。-Normal and kernel density estimation using sub-digit calculation. Including the normal quantile function, kernel estimate probability density function, cumulative distribution probabilit
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视频背景非参数估计论文及matlab实现.matlab代码只实现了灰度图的背景估计,论文利介绍的彩色视频处理方法可以自己看看怎么做。-Background and Foreground Modeling Using
Nonparametric Kernel Density Estimation for
Visual Surveillance
AHMED ELGAMMAL, RAMANI DURAISWAMI, MEMBER, IEEE, DAVID HARWOOD, AND
LA
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kde全称是kernel density estimation.基于核函数的概率密度估计方法。是模式识别中常用的算法之一-KDE which is kernel density estimation is used to estimate probabilty function. It is mostly used in pattern recogntion
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Kernel Density Estimation (Set of tools for nonparametric (kernel) density estimation)
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本程序是一维密度估计,用于了解和密度估计有很大帮助。-One-dimensional kernel density estimation
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KDE function or kernel density function for the estimation of a continuous density
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基于非参数核密度估计的Copula函数选择原理.-Based on nonparametric kernel density estimation in the Copula function selection principle
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把那个软件包解压缩,添加到matlab路径下,运行@kde\mex\makemex.m,然后就可以调用kde函数创建核密度估计类对象,调用类对象的各种“方法”实现核密度估计、画图等-Next, running @ kde \ mex \ makemex. then can be called kde function creates kernel density estimation class object, the call of the object of "method" realiz
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machine learning-Density Estimation objects.
parzen - Parzen s windows kernel density estimator
indep - Density estimator which assumes feature independence
bayes - Classifer based on density estimation for each class
gauss - Normal distr
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Locally Adaptive Kernel Density Estimation
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Locally Adaptive Kernel Density Estimation
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核密度估计,可以刻画变量的时间演进动态性,可以反映变量在时间上的变动的整体性- Kernel Density Estimation
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kernel density estimation
MATLAB
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核密度估计的parzen窗法,简单易用,适合于初学非参数估计的用户。-Kernel Density Estimation parzen window method, easy to use, suitable for novice non-parametric estimation of the user.
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利用Gauss核密度估计求窗口宽度,只需导入源数据和修改窗口宽度即可计算最优的窗口宽度,方便,简单,实用-Use Gauss kernel density estimation window width requirements, simply import the source data and modify the window width to calculate the optimal width of the window, convenient, simple, practical
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对6个样本点,进行直方图估计核高斯核密度估计-for 6 sample points, histogram estimation and Gauss kernel density estimation
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计算核密度估计的一个程序,内置多种函数。可以修改原始数据。-Computing kernel density estimation program built many functions. You can modify the original data.
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本程序是核密度函数的运用。对于大量的数据,而且分部成离散状态时,采用核密度分析是有效的。-This procedure is to use kernel density function. For large amounts of data into discrete segments and state, the use of nuclear density analysis is valid.
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