Description: 采用期望最大算法最优化初始高斯混合模型的程序，笔者借用ubm自适应方法，很好的解决了模型不收敛的问题。-expectations largest algorithm using optimization initial Gaussian mixture model procedures, the author borrowed ubm adaptive method, a good model is not the solution convergence problem. Platform: |
Size: 4096 |
Author: |
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Description: 机器学习中的E M算法，本代码是基于高斯混合模型的E M 算法聚类。-machine learning algorithm E M, the code is based on the Gaussian mixture model clustering algorithm E. M. Platform: |
Size: 4096 |
Author:李民 |
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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: 使用高斯模型期望值最大化演算法,做圖形分割
Gaumix_EM: EM Algorithm Applicated to Parameter Estimation for Gaussian Mixture
-Gaussian model using expectation maximization algorithm, to do graphics segmentation Gaumix_EM: EM Algorithm Applicated to Parameter Estimation for Gaussian Mixture Platform: |
Size: 1024 |
Author:李致賢 |
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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)
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％ 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 |
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Description: 减背景算法，基于背景建模的方法获取前景目标，采用高斯混合模型-By the background algorithm, based on background modeling method to get the prospect of goals, the use of Gaussian mixture model Platform: |
Size: 1732608 |
Author:曾慕柳 |
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Description: 基于Matlab的高斯混合模型的算法实现，该程序可以实现对图像处理的功能-Matlab based on the Gaussian mixture model algorithm, the program can achieve the functions of image processing Platform: |
Size: 5120 |
Author:geyu |
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Description: :高斯混合模型(GMM)是一种经典的说话人识别算法，本文在实现其算法的同时，主要模拟了不同噪声环境情况下高斯混合模型
(GMM)的杭嗓声性能，得到了一些有益结论。
-Gaussian mixture model (GMM) is a classic speaker recognition algorithms, this algorithm at the same time in fulfilling its main simulated environmental conditions under different noise Gaussian mixture model
(GMM) of the Hang throat sound performance, and obtained some useful conclusions.
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Size: 119808 |
Author:于高 |
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Description: 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. Platform: |
Size: 6144 |
Author:ruso |
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Search - ALGORITHM GAUSSIAN MIXTURE