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一个在matlab环境下编写的采用expectation maximization方法计算高斯混合模型的程序。
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用matlab语言写的EM(Expectation maximization)算法,用于模式分类
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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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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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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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关于最大似然重建方法的实现，可用于tomography reconstruction-This is the code for maximum likelihood expectation maximum reconstruction method which is frequently applied in tomography reconstruction, such as CT and PET
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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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Linear dynamical system. This set of functions performs inference and learning of a linear Kalman filter model. Inference is carried out via forward-backward smoothing, and learning is accomplished 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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采用EM，期望最大方法，估计QPSK调制方式下的信噪比-Using EM, expectation maximization method to estimate the signal to noise ratio under QPSK modulation
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In statistics, an expectation-maximization (EM) algorithm is a method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM is an iterati
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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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求解参数估计的常用算法——EM，即期望最大化算法，用于代替样本量不完全时的极大似然估计算法。-Common algorithm for solving parameter estimation- EM, expectation maximization algorithm is used to replace the sample size is not completely at the maximum likelihood estimation algorithm.
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EM算法，最大期望值算法，用MATLAB程序来编程-EM algorithm, expectation maximization algorithm using MATLAB program to program
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