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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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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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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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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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k-means 算法接受输入量 k ；然后将n个数据对象划分为 k个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个“中心对象”（引力中心）来进行计算的。-In statistics and machine learning, k-means clustering is a method of cluster analysis which aims to partition n observations into
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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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GUI for an Expectation-Maximization algorithm (EM) variant (Split-EM-Discriminant)
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In statistics and machine learning, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. It is similar to the expectation-max
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This is Expectation Maximization algorithm code.
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期望最大化的算法代码，类似于k近邻，分为两个步骤：E步骤和M步骤。-Expectation maximization algorithm code, similar to the k nearest neighbor, is divided into two steps: E steps and M steps.
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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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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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