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training_gmm
- 训练高斯混合模型的程序，尽管此类代码较多，但本程序经过笔者改写优化后，很大程度上避免了普通方法中局部最优的问题。-Gaussian mixture model training procedures, although the code more, but the procedure after the author rewrite optimization, largely avoiding the ordinary method o
gmm_creation_mle
- 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 c
gmm_prob
- 计算高斯混合模型先验概率和后验概率的程序，采用大矩阵运算，大大提高了运行速度。-Gaussian mixture model calculated a priori probability and the probability of post-mortem procedures, using a large matrix computation, greatly improved speed.
E_M_matlab
- 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.
ClusteringToolboxandPDFintroduce
- 这里包含了聚类的工具箱还有很详细的文档说明，以及包含高斯混合模型的源程序。-This toolbox contains a cluster of documents are very detailed descr iption of the Gaussian mixture model that contains the source code.
EMGMM
- 混合高斯模型和EM算法结合，当中用到了自己写的Kmeans聚类，附带测试样例、训练样例和main函数。-Gaussian Mixture Model and EM algorithms, which use their own written Kmeans cluster, with the test sample, the training sample and the main function.
gaussmix
- 混合高斯模型 对于给定的数据，可以自动选择最佳聚类数目和聚类中心，并根据判决规则进行收敛，运算很快，非常方便-Mixed-Gaussian model for a given data, can automatically select the best cluster number and cluster centers, and in accordance with decision rules convergence, com
GMM
- 实现混合高斯模型的聚类算法 利用最大似然估计和最大期望的方法来实现混合高斯模型-Gaussian mixture model to achieve clustering algorithm using the maximum likelihood estimation and the greatest way to achieve the desired mixed-Gaussian model
codes
- 这个文件包里包含了几乎所有的聚类的工具箱，包含采用混合高斯模型的运动物体检测方面的源代码。-This document contains the bag almost all the clustering of the toolbox, including the use of Gaussian Mixture Model of Moving Objects for testing source code.
cluster
- 用高斯混合模型进行数据聚类分析的matlab 程序。-Set of files for analysis of Gaussian mixture models for data set clustering etc.
walkstraight
- 利用K均值聚类的方法实现人体的检测，混合高斯模型建立背景并实时更新-The use of K-means clustering method to achieve the human body detection, Gaussian mixture model to establish the background and real-time updates
gaussmix(Bouman)M
- 高斯混合模型用于聚类的程序。可以直接使用。里面有3个例子。-Gaussian mixture model for the clustering process. Can be used directly. Inside there are three examples.
DxSampleCxImage
- GMM GMM高斯混合模型聚类 Gaussian mixture model clustering-GMM GMM Gaussian mixture model clustering
高斯混合模型EM算法MATLAB程序
- 在高斯混合模型上实现聚类问题的算法。将2个高斯混合，然后尝试学习两个高斯混合后的参数。(Algorithm for clustering problem on Gauss mixture model. Mix the 2 Gauss and then try to learn the parameters after the two Gauss mixing.)
GMM
- 此算法实现高斯混合，可以对初始聚类算法选择c均值和EM，可以实现密度估计和分类。(This GMM algorithm can estimate the density and class, the initial steps can select the C-mean and EM.)
Gaussian Mixture Model Ellipsoids
- 基于2个一维高斯模型组成的多维混合高斯模型，采用Python进行编程(Multidimensional mixed Gauss model based on 2 one-dimensional Gauss models and programming with Python)
GMM
- 高斯混合聚类的python实现代码，里面有data的demo(Python implementation code of Gauss mixed clustering)
GMM
- 实现了EM算法对高斯混合模型进行聚类，并将聚类结果用图像展示出来，希望对混合模型的朋友有用。(The EM algorithm is implemented to cluster the Gauss mixture model, and the clustering results are displayed with images, hoping to be useful to friends of the mixed models.
RCY-GMMtest1
- 高斯混合模型（GMM,Gaussian Mixture Model）参数如何确立这个问题，详细讲解期望最大化（EM,Expectation Maximization）算法的实施过程。(How to establish the parameters of Gauss mixture model and explain the implementation process of the expectation maximization al
BIC确定GMM聚类簇数
- 通过贝叶斯信息准则确定高斯混合聚类方法的聚类簇数(Determining the Cluster Number of GMM Clusters by BIC)