Search - EM algorithm K gaussian

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[Other resource] EM_GM DL : 0: ％ 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 ％
Update : 2008-10-13 Size : 3.34kb Publisher : Shaoqing Yu
[matlab] EM_GM DL : 0: ％ 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
Update : 2025-02-19 Size : 3kb Publisher : Shaoqing Yu
[AI-NN-PR] EM_GM DL : 0: 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
Update : 2025-02-19 Size : 1kb Publisher : scaning
[matlab] GM_EM DL : 0: 不错的GM_EM代码。用于聚类分析等方面。- GM_EM- fit a Gaussian mixture model to N points located in n-dimensional space. Note: This function requires the Statistical Toolbox and, if you wish to plot (for k = 2), the function error_ellipse Elementary usage: GM_EM(X,k)- fit a GMM to X, where X is N x n and k is the number of clusters. Algorithm follows steps outlined in Bishop (2009) Pattern Recognition and Machine Learning , Chapter 9. Additional inputs: bn_noise- allow for uniform background noise term ( T or F , default T ). If T , relevant classification uses the (k+1)th cluster reps- number of repetitions with different initial conditions (default = 10). Note: only the best fit (in a likelihood sense) is returned. max_iters- maximum iteration number for EM algorithm (default = 100) tol- tolerance value (default = 0.01) Outputs idx- classification/labelling of data in X mu- GM centres
Update : 2025-02-19 Size : 3kb Publisher : 朱魏
[Graph program] EM-suanfa-hunhegaosi DL : 0: em算法计算混合高斯模型的参数估计，极大似然，EM算法用于K均值问题的参数估计。MATLAB实现有代码-em algorithm Gaussian mixture model parameter estimation, maximum likelihood parameter estimation for K-means problem EM algorithm. MATLAB implementation code
Update : 2025-02-19 Size : 223kb Publisher : 林
[matlab] EM_GM DL : 0: EM algorithm for k multidimensional Gaussian mixture estimation
Update : 2025-02-19 Size : 3kb Publisher : Леля
[Editor] EM_GM DL : 0: EM algorithm for k multidimensional Gaussian mixture estimation
Update : 2025-02-19 Size : 2kb Publisher : hagacom
[Other] EM-Probability DL : 0: 多维的概率估算,用EM算法,可直接使用.-EM algorithm for k multidimensional Gaussian mixture estimation
Update : 2025-02-19 Size : 2.06mb Publisher : 甘继来