Description: ％ EM algorithm for k multidimensional Gaussian mixture estimation
％
％ Inputs:
％ X(n,d) - input data, n=number of observations, d=dimension of variable
％ k - maximum number of Gaussian components allowed
％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none)
％ maxiter - maximum number of iteration allowed ([] for none)
％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none)
％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none)
％
％ Ouputs:
％ W(1,k) - estimated weights of GM
％ M(d,k) - estimated mean vectors of GM
％ V(d,d,k) - estimated covariance matrices of GM
％ L - log likelihood of estimates
％ Platform: |
Size: 3416 |
Author:Shaoqing Yu |
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Description: The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application.-The software implements particle filteri Vi and Rao Blackwellised particle filtering az r conditionally Gaussian Models. The RB algori thm can be interpreted as an efficient stochast ic mixture of Kalman filters. The software also includes efficient state-of-the-art resampl ing routines. These are generic and suitable az r any application. Platform: |
Size: 130048 |
Author:大辉 |
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Description: EM算法处理高斯混和模型，是用MATLAB实现的-EM algorithm for Gaussian mixture model of treatment is achieved using MATLAB Platform: |
Size: 1024 |
Author:李晋博 |
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Description: EM算法是机器学习领域中常用的一种算法，这个文件是EM算法最简单的一种实现，即在Gaussian Mixture model上面的EM。-EM field of machine learning algorithm is commonly used in an algorithm, this document is the most simple EM algorithm as a realization that, in Gaussian Mixture model above EM. Platform: |
Size: 3072 |
Author:De-Chuan Zhan |
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Description: ％ EM algorithm for k multidimensional Gaussian mixture estimation
％
％ Inputs:
％ X(n,d) - input data, n=number of observations, d=dimension of variable
％ k - maximum number of Gaussian components allowed
％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none)
％ maxiter - maximum number of iteration allowed ([] for none)
％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none)
％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none)
％
％ Ouputs:
％ W(1,k) - estimated weights of GM
％ M(d,k) - estimated mean vectors of GM
％ V(d,d,k) - estimated covariance matrices of GM
％ L - log likelihood of estimates
％- EM algorithm for k multidimensional Gaussian mixture estimation Inputs: X (n, d)- input data, n = number of observations, d = dimension of variable k- maximum number of Gaussian components allowed ltol- percentage of the log likelihood difference between 2 iterations ([] for none) maxiter- maximum number of iteration allowed ([] for none) pflag- 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) Init- structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) Ouputs: W (1, k)- estimated weights of GM M (d, k)- estimated mean vectors of GM V (d, d, k)- estimated covariance matrices of GM L- log likelihood of estimates Platform: |
Size: 3072 |
Author:Shaoqing Yu |
Hits:
Description: 最近在做毕设，是有关高斯混合模型的算法，主要采用EM算法，这片硕士论文在这方面介绍的比较详细，可以去下载研究下。-Recently completed the set up to do, is the Gaussian mixture model algorithm, the main use of EM algorithm, this Master Platform: |
Size: 1801216 |
Author: |
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Description: 本算法包括最大似然估计，最小二乘估计，基于EM算法的多种混合高斯分布估计，EM算法测试实例，绘制每种分布的plot函数。非常有参考价值！-This algorithm, including maximum likelihood estimation, least squares estimation, based on the EM algorithm estimate a mixture Gaussian distribution, EM algorithm for the test examples, each mapping the distribution of the plot function. Very useful! Platform: |
Size: 22528 |
Author:liyuedsg |
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Description: 这是一款用C++编写的实现gmm算法的程序，有样例程序，实现了EM算法来寻找GMM参数。-This is a used C++ Prepared gmm algorithm to achieve the procedure, there are sample procedures, the realization of the EM algorithm to find GMM parameters. Platform: |
Size: 32768 |
Author:阿商 |
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Description: 多种概率分布的拟合函数集合
本算法包括最大似然估计，最小二乘估计，基于EM算法的多种混合高斯分布估计，EM算法测试实例，绘制每种分布的plot函数。非常有参考价值！-Fitting a wide range of probability distribution functions including the collection of this algorithm maximum likelihood estimation, least squares estimation, based on the EM algorithm estimate a mixture Gaussian distribution, EM algorithm for the test examples, each mapping the distribution of the plot function. Have reference value is! Platform: |
Size: 21504 |
Author:lcp |
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Description: EM算法（英文）A Gentle Tutorial of the EM Algorithm
and its Application to Parameter
Estimation for Gaussian Mixture and
Hidden Markov Models-A Gentle Tutorial of the EM Algorithm
and its Application to Parameter
Estimation for Gaussian Mixture and
Hidden Markov Models Platform: |
Size: 99328 |
Author:雷雷 |
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Description: EM算法简明教程 用于高斯分布隐马尔可夫模型的参数估计-Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models Platform: |
Size: 99328 |
Author:hou |
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Description: 利用Matlab编程验证用EM算法估计的高斯混合模型的相关参数的性能。-Validate the use of Matlab programming estimated using EM algorithm for Gaussian mixture model parameters related to the performance. Platform: |
Size: 1024 |
Author:娜张纪念 |
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Description: EM算法用于混合高斯模型的参数估计，并附有一个例子进行说明，程序解释-EM algorithm is used for of the gaussian mixture model parameter estimation, and with an example specification, process explanation Platform: |
Size: 1024 |
Author:唐苦 |
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Description: EM算法用于高斯混合模型，实现数据的精确分类-The EM algorithm for Gaussian mixture model, the exact classification of the data Platform: |
Size: 1024 |
Author:李伟 |
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Description: A Gentle Tutorial of the EM Algorithm
and its Application to Parameter
Estimation for Gaussian Mixture and
Hidden Markov Models Platform: |
Size: 88064 |
Author:asif |
Hits:
Description: 程序展示了使用EM algorithm来训练GMM(Gaussian Mixture Model)来进行binary classification。-Program demonstrates the use of EM algorithm to train the GMM (Gaussian Mixture Model) for binary classification. Platform: |
Size: 3072 |
Author:杜晗 |
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Description: Just like EM of Gaussian Mixture Model, this is the EM algorithm for fitting Bernoulli Mixture Model.
GMM is useful for clustering real value data. However, for binary data (such as bag of word feature) Bernoulli Mixture is more suitable. Platform: |
Size: 3072 |
Author:lin |
Hits:
Description: 一个EM算法 这是通过高斯混合模型进行聚类的EM算法的实现，使用图形在线表示。(EM algorithm
This is an implementation of the EM algorithm for Clustering via Gaussian mixture models, using graphical on-line representation.) Platform: |
Size: 2048 |
Author:ykkhds
|
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Search - EM algorithm for mixture of Gaussian