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Expectation-Maximization
The EM (Expectation-Maximization) algorithm estimates the parameters of the multivariate probability density function in a form of the Gaussian mixture distribution with a specified number of mixtures.
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统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含：
1，Analysis of linear discriminant function
2，Feature extraction: Linear Discriminant Analysis
3，Probability distribution estimation and clustering
4，Support Vector and other Kernel Machines,
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Source code - create Gaussian Mixture Model in following steps:
1, K-means
2, Expectation-Maxximization
3, GMM
Notice: All datapoints are generated randomly and you can config in Config.h-Source code- create Gaussian Mixture Model
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最大的高斯混合模型似然估计的期望最大化算法-Maximum likelihood estimation of Gaussian mixture model by expectation maximization algorithm
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Free Split and Merge Expectation-Maximization algorithm for Multivariate Gaussian Mixtures. This algorithm is suitable to estimate mixture parameters and the number of conpounds-Free Split and Merge Expectation-Maximization algorithm for Multivariate
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GMM-GMR is a set of Matlab functions to train a Gaussian Mixture Model (GMM) and retrieve generalized data through Gaussian Mixture Regression (GMR). It allows to encode efficiently any dataset in Gaussian Mixture Model (GMM) through the use of an Ex
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Bayesian mixture of Gaussians. This set of files contains functions for performing inference and learning on a Bayesian Gaussian mixture model. Learning is carried out via the variational expectation maximization algorithm.
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Mixture of linear regressors. The routines contained in this file allow inference and learning of a mixture of linear-Gaussian regression models. Learning is performed by maximizing the data likelihood via the expectation maximization algorithm.
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This matlab code implements the Expectation-Maximization algorithm to estimate the parameters of a gaussian mixture model.
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文章展示了基于高斯混合模型的语音频谱预测方法。频谱预测可能在传包过程中预防丢包这方面起到大作用。期望最大化算法用两倍或三倍的连续语音因素来测试模型。模型被用来设计第一，儿等指令预测量。预测表用频谱分配状态来估计并和一个简单的参考模型对比。最好的预测表得到一个平均频率扭曲值是0.46dB小于参考模型-This paper presents methods for speech spectrum prediction based
on Gaussian mixture models. Spec
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基于matlab的SVM（支持向量机）算法。作为非常流行的svm工具，可以实现基于SVM的数据分析，能够应用于人工智能及模式识别领域。-Matlab based on the expectation-maximization algorithm for Gaussian mixture model (GMM) toolkit. GMM-based data can be analyzed, can be used in the field of artificial intelligence a
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Traditional single particle reconstruction methods use either the Fourier or the
delta function basis to represent the particle density map. We propose a more
flexible algorithm that adaptively chooses the basis based on the data. Because
the b
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基于Expectation Maximization算法优化的高斯混合模型在3D图像数据聚类中的应用。-This is a 3D visualization of how the Expectation Maximization algorithm learns a Gaussian Mixture Model for 3-dimensional data.
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This introduction to the expectation–maximization (EM) algorithm
provides an intuitive and mathematically rigorous understanding of
EM. Two of the most popular applications of EM are described in
detail: estimating Gaussian mixture models (GMMs),
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了适应跟踪过程中目标光照条件的变化，并对目标特征进行在线更新，提出一种将局部二元模式(LBP)
特征与图像灰度信息相融合，同时结合增量线性判别分析对目标进行跟踪的算法．跟踪开始前，为了获得比较准确的目标描述，使用混合高斯模型和期望最大化算法对目标进行分割；跟踪过程中，通过蒙特卡罗方法对目标区域和背景区域进行采样，并更新特征空间参数．得到目标和背景的最优分类面；最后使用粒子滤波器结合最优分类面对目标状态进行预测．通过光照变化的仿真视频和自然场景视频的跟踪实验，验证了文中算法的有效性．-Trac
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这篇论文基于Kernel和Gaussian Mixture Model提出了一种新的误码率估计方法，使用Stochastic Expectation-Maximization算法实现误码率实时盲估计-This report presents a new Bit Error Rate estimation method based on Kernel and Gaussian Mixture Model, the method utilizes Stochastic Expectation-Max
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经典的em算法即期望最大化算法，可用于高斯混合GMM模型和聚类算法，-Classic em algorithm that expectation maximization algorithm can be used for Gaussian mixture models and GMM clustering algorithm,
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Expectation Maximization (EM) Algorithm for Gaussian Mixture Model
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Expectation Maximization algorithm for Gaussian Mixture Model Training
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基于高斯混合模型和EM（Expectation Maximization）算法的SAR影像变化监测算法，并附带示例。总体思路是首先将两个时期的SAR影像做log和ratio运算，生成差分影像，然后通过EM算法估计高斯混合模型的参数，最后根据高斯混合模型最大概率，生成变化监测结果。-Unsupervised change detection method for SAR images using EM algorithms of Gaussian mixture model
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