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對model做expectation maximization
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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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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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Blobworld:基于期望最大算法的图像分割
及其在图像查询中的应用
-Blobworld: Image segmentation using Expectation-Maximization and its
application to image querying
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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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duke的tutorial on EM的matlab经典源码，值得一看。-Matlab code for the tutorial on Expectation Maximization，worth a visit.
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I present an expectation-maximization (EM) algorithm for principal
component analysis (PCA).
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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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It actually simulates the registration process of multiple dissimilar sensors in a wireless sensor network using the expectation maximization algorithm.
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Expectation Maximization for training GMM s, diagonal covariances. Requires vqtrain.m to have a good initialization.
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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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Expectation Maximization
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This is Expectation Maximization algorithm code.
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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 coin toss examples.rar
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