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The EM algorithm is short for Expectation-Maximization algorithm. It is based on an iterative optimization of the centers and widths of the kernels. The aim is to optimize the likelihood that the given data points are generated by a mixture of Gaussi
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期望最大化算法。MALAB编写。应用用模式识别。-expectation maximization algorithm. MATLAB prepared. Application of pattern recognition.
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非常好的EM算法介绍,不妨去看看.The Expectation Maximization Algorithm
A short tutorial
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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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混合高斯分布中基于最大期望算法的参数估计模型，适应于通信与信号处理以及统计学领域,Mixed Gaussian distribution algorithm based on the parameters of the greatest expectations of the estimated model, adapted to communications and signal processing, as well as the field of statistics
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EM算法介绍及Matlab演示代码(一维和多维高斯混合模型学习算法)-Introduction of EM algorithm and Matlab codes that implement the algorithm
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用于估计未知数据的EM算法，即最大期望算法，用到的地方很多，可用来做同步。-The data used to estimate the unknown EM algorithm, that is the maximum expectation algorithm, used in many places, can be used for synchronization.
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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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The k-means algorithm is an algorithm to cluster n objects based on attributes into k partitions, k < n. It is similar to the expectation-maximization algorithm for mixtures of Gaussians in that they both attempt to find the centers of natural clu
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基于em算法的图象分割程序,是图象分割的一类重要算法.-Em algorithm based on image segmentation process of image segmentation are essential for a class of algorithms.
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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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This package contains Matlab m-files for learning finite Gaussian mixtures from sample data and performing data classification with Mahalanobis distance or Bayesian classifiers. Each class in training set is learned individually with one of the three
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Expectation-maximization algorithm
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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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This is Expectation Maximization algorithm code.
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EM算法 , EM算法是机器学习中一个很重要的算法，即期望最大化算法-EM algorithm, EM algorithm is a very important machine learning algorithm, that is, expectation maximization algorithm
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Expectation-Maximization algorithm
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Expectation-Maximization algorithm for a HMM with Multivariate Gaussian measurement
Usage
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[logl , PI , A , M , S] = em_ghmm(Z , PI0 , A0 , M0 , S0 , [options])
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Expectation Maximization algorithm for Gaussian Mixture Model Training
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