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Description: 混合高斯分布EM算法，可以算混合高斯分布的三个参数。混合高斯分布更接近系数分布。-mixed Gaussian distribution EM algorithm can calculate mixed Gaussian distribution of the three parameters. Mixed closer to the Gaussian distribution coefficient.
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Size: 8972 |
Author: 连强 |
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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。
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Size: 3285 |
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
％
Platform: |
Size: 3416 |
Author: Shaoqing Yu |
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Description: 混合高斯分布EM算法，可以算混合高斯分布的三个参数。混合高斯分布更接近系数分布。-mixed Gaussian distribution EM algorithm can calculate mixed Gaussian distribution of the three parameters. Mixed closer to the Gaussian distribution coefficient.
Platform: |
Size: 9216 |
Author: 连强 |
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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
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Size: 3072 |
Author: Shaoqing Yu |
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Description: EM_GM模型的MATLAB实现,在语音识别方面很有用！-MATLAB model EM_GM realization aspects in speech recognition is useful!
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Size: 3072 |
Author: 简菱 |
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Description: 针对于K维高斯混合模型估计的期望最大算法-EM algorithm for k multidimensional Gaussian mixture estimation
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Size: 1024 |
Author: scaning |
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Description: em算法的介绍和一个 游泳的事例是大家学习交流的很好的程序请大家多顶-good
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Size: 9216 |
Author: 胖子 |
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Description: EM算法实现,是matlab主站上获得的可靠程序。-EM algorithm，which is from the matlab net about image processing.
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Size: 40960 |
Author: 朱亮亮 |
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Description: Smoothed bootstrap Newcomb s speed-of-light data are used in the book Bayesian Data Analysis b
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Size: 3072 |
Author: xzhe |
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Description: algorithm EM for gaussian mixture
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Size: 3072 |
Author: soumia |
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Description: 这是关于说话人识别很好的源程序，对研究GMM模型的说话人识别有很好的价值。-They are programms about speaker recognition, and they are helpful to the study about GMM speaker recognition.
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Size: 33792 |
Author: laiyuping |
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Description: this is expectation maximize algorithm for guassian mixture
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Size: 3072 |
Author: mohsen |
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Description: EM算法和高斯模型的，用Matlab编程工具编写，实现功能快速-EM algorithm and the Gaussian model, using Matlab programming tools to write, to achieve rapid function
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Size: 45056 |
Author: xiajulong |
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Description: gaussion medel
function [W,M,V,L] = EM_GM(X,k,ltol,maxiter,pflag,Init)
[W,M,V,L] = EM_GM(X,k,ltol,maxiter,pflag,Init) -gaussion medel
function [W,M,V,L] = EM_GM(X,k,ltol,maxiter,pflag,Init)
[W,M,V,L] = EM_GM(X,k,ltol,maxiter,pflag,Init)
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Size: 3072 |
Author: cuicui |
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Description: EM algorithm for Gaussian Mixture Model
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Size: 3072 |
Author: Muhammad Bilal |
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Description: EM algorithm for k multidimensional Gaussian mixture estimation
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Size: 3072 |
Author: Леля |
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Description: EM algorithm for k multidimensional Gaussian mixture estimation
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Size: 2048 |
Author: hagacom |
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Description: EM algorithm for Gaussian mixture (EM_GM) learning is well known
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Size: 4096 |
Author: hagacom |
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