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Adaptive Mixtures Density Estimation的matlab实现-Adaptive Mixtures Density Estimation of ma tlab achieve
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该算法是经典的信噪比估计算法——最大似然估计算法，利用接收信道的先验概率密度函数，ML法能够很好的估计信号的信噪比,The algorithm is a classic signal to noise ratio estimation algorithm- maximum likelihood estimation algorithm, using the a priori receiver channel probability density function, ML method can
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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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是有关机器学习中讲密度估计的好资料，自己找了很久才找到的，国外人写的很容易懂，乐意分享-Machine learning is related to good information about density estimation, for a long time to find your own, and foreign people to write easy to understand, are willing to share
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二维数据集Parzen方窗非参数估计PDF(概率密度函数)，三维结果显示，有图，有完整说明文档和程序运行说明，matlab编程环境，此为模式识别小作业 parzen-Dimensional data set Parzen Window non-parametric estimation side PDF (probability density function), three-dimensional results show that map, with complete documentat
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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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Non-parametric density estimation
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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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利用支持向量回归进行概率密度估计，从而计算信息熵-The use of support vector regression for probability density estimation, in order to estimate the information entropy
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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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parzen window density estimation with Gaussian as a smoothing factor
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Kernel Density Estimation (Set of tools for nonparametric (kernel) density estimation)
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kernal density function for image processing and probability density function estimation
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KDE function or kernel density function for the estimation of a continuous density
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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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kernel density estimation
MATLAB
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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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给定若干三维数据，建立训练概率模型，并对新数据进行估计。包括高斯模型、Parzen窗和K近邻密度估计-Given a number of three-dimensional data, the establishment of training probability model, and the new data is estimated. Including the Gaussian model, Parzen windows and K nearest neighbor density e
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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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