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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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机器人灵巧手的抓持分类是抓持规划的一个主要问题．本文应用模式识别技术设计和实现了一种基于高斯混合模型GMM 的分类器．采用Expectation Maximization(EM)算法估计GMM 的参数，对人手的抓持动作进行识别与分类，经过人手到机器人手的关节空间运动映射。
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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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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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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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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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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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Expectation Maximization for training GMM s, diagonal covariances. Requires vqtrain.m to have a good initialization.
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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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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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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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Expectation-Maximization algorithm
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In this paper, we propose a Bayesian methodology for
receiver function analysis, a key tool in determining the deep structure
of the Earth’s crust.We exploit the assumption of sparsity for
receiver functions to develop a Bayesian deconvolution
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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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自己编写的期望最大化（EM）算法的MATLAB实现，里面有较为运行方法和程序说明，对新手有很好的帮助-
I have written the expectation-maximization (EM) algorithm in MATLAB, which has run more methods and procedures described, there is a good help for the novice
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在统计计算中，最大期望（EM）算法是在概率（probabilistic）模型中寻找参数最大似然估计或者最大后验估计的算法，其中概率模型依赖于无法观测的隐藏变量（Latent Variable）。最大期望经常用在机器学习和计算机视觉的数据聚类（Data Clustering）领域。(In statistical calculation, the expectation maximization (EM) algorithm in probability (probabilistic) maximu
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介绍了分布式em在DOA中的应用，含有不同信噪比的实验结果(Introduced the application of distributed em in DOA)
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