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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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详细介绍MCL算法，是由Sebastian Thrun a, Dieter Fox, Wolfram Burgard, Frank Dellaert所著的论文，发表于Artificial Intelligence上。-Mobile robot localization is the problem of determining a robot’s pose from sensor data. This
article presents a family of probabilistic lo
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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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视频跟踪检测 背景核密度估计建模，欢迎指正，-Detection of the background video track modeling kernel density estimation, please correct me, thank you
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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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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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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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机器学习matlab源代码，包括多分类SVM，模式识别，特征选择，回归等算法。-The spider is intended to be a complete object orientated environment for machine learning in Matlab. Aside from easy use of base learning algorithms, algorithms can be plugged together and can be compared with
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kernel density estimation
MATLAB
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Matlab implementation of the Kernel Density estimation for 2D array/matrix
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波动率曲面matlab实现，可应用于期权市场上的任意期权。(The function VolSurface.m will then:
- compute and output the Black-Scholes implied volatility (this will be a matrix).
- get and plot the corresponding volatility surface using a kernel (Gaussian) density estimation.)
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