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The subroutines glkern.f and lokern.f use an efficient and fast algorithm for
automatically adaptive nonparametric regression estimation with a kernel method.
Roughly speaking, the method performs a local averaging of the observations when
es
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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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一种图像去噪的新方法，非参数估计，值得一看。-A new method for image denoising, nonparametric estimation, worth a visit.
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"This graduate textbook covers topics in statistical theory essential for graduate students preparing for work on a Ph.D. degree in statistics. The first chapter provides concepts and results in measure-theoretic probability theory that are useful in
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Bartlett power spectrum estimation (Nonparametric methods for power spectrum estimation)
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Kernel Density Estimation (Set of tools for nonparametric (kernel) density estimation)
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Modern Spectrum Estimation
Nonparametric Methods
Minimum Variance Spectrum Estimation etc.
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：Mean—shift算法是一种非参数密度估计算法，可以实现快速的最优匹配，在目标的实时跟踪领域起着非常
重要的作用。为了有效的将Mean—shift算法应用到灰度图像中，采用了以方向直方图建立目标模型的策略，提出了在灰
度图像中以Mean—shift为核心的目标跟踪算法。实验结果表明，该算法具有不受光照条件影响的优点，在低对比度的情
况下仍能实现稳定、实时的跟踪目标。-: Mean-shift algorithm is a nonparametric density estimat
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数学建模中对交通流量短时间预测 主要基于非参数估计回归-Mathematical Modeling of short-term traffic forecast is based on nonparametric regression estimation
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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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A blind source separation algorithm is proposed
that is based on minimizing Renyi’s mutual information by means
of nonparametric probability density function (PDF) estimation.
The two-stqge process consists of spatial whitening
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The mean-square efficiency of cumulative distribution function estimation can be improved through
kernel smoothing. We propose a plug-in bandwidth rule, which minimizes an estimate of the asymptotic
mean integrated squared error.
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关于惩罚样条的非参数估计方法，利用样条函数估计非参数模型-Non-parametric estimation methods of punishment on the spline, using spline nonparametric estimation model
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Parzen窗估计法是一种非参数函数估计方法，它能够较好地描述多维数据的分布状态。其基本思想就是利用一定范围内各点密度的平均值对总体密度函数进行估计。-Parzen window estimation method is a nonparametric function estimation method, which can describe the distribution of multi-dimensional data. The basic idea is to use the ave
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均值漂移算法是一种基于颜色特征的无参密度估计算法，被人们广泛的应用于图像滤波、聚类分析和目标跟踪等领域。-Mean shift algorithm is a nonparametric density estimation algorithm based on color feature, which is widely used in the fields of image filtering, clustering analysis and target tracking.
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基于k近邻估计法的非参数概率密度估计论文及源代码的如何实现-K-nearest neighbor nonparametric probability density estimation method based on the estimated
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用非参数估计的方法（核密度估计）来估计互信息-Nonparametric estimation method (kernel density estimation) to estimate the mutual information
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本实验的目的是学习Parzen窗估计和k最近邻估计方法。在之前的模式识别研究中，我们假设概率密度函数的参数形式已知，即判别函数J(.)的参数是已知的。本节使用非参数化的方法来处理任意形式的概率分布而不必事先考虑概率密度的参数形式。在模式识别中有躲在令人感兴趣的非参数化方法，Parzen窗估计和k最近邻估计就是两种经典的估计法。(The purpose of this experiment is to study the Parzen window estimation and the k nea
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用于计算样本数据的非参数核密度估计，代码简单易懂。(Nonparametric kernel density estimation)
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