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高斯混合模型的EM算法的源程序代码
- GMM模型的一个小例子，可以做出不学习这个模型用
GMM-modeling-and-EM
- 介绍Opencv这个图像处理库环境下的GMM建模与EM算法，从数学的角度深入分析高斯建模和EM算法-Introduce the the Opencv this image processing GMM modeling the EM algorithm in the library environment, in-depth analysis from a mathematical point of the Gaussian model and the EM algorithm
08gmm
- GMM很好的理论资料,对高斯模型的详细描述以及EM算法的介绍。对编程有一定的帮助。-This is for the initial researcher to study about the GMM Model.
GMM_background_src
- 基于有限混合高斯模型的数据分类 1、使用基于有限高斯混合模型的EM算法对数据样本进行归类 2、使用C++或者Matlab语言编程环境实现该算法，并用给定的数据包对算法的正确性进行检验 -Gaussian mixture model based on limited data classification 1, using the finite Gaussian mixture model based on EM algorithm to classify the data sam
GMM-GMR-v2.0
- 基于泛化自回归的源模型，以及在这个模型基础上的EM算法实现-Generalization based on the source from the regression model, as well as in the model based on EM algorithm
Density_Estimation
- 分别采用GMM和KDE对Iris数据集进行密度建模，并进行对比。通过EM算法来确定GMM参数，通过交叉验证来确定K值-GMM and KDE respectively Iris data set of density modeling, and compared. GMM by EM algorithm to determine the parameters of K determined by the value of cross-validation
em
- EM算法的Matlab实现，针对GMM模型-EM algorithm for GMM Model
GMM
- 这里介绍了一个利用了EM算法得到的GMM训练c代程序-This is a useful file of GMM code
EM
- GMM的优化，用EM算法实现高斯混合模型-GMM optimization algorithm EM
EM-GMM
- 利用EM算法实现高斯混合模型的优化，完成特征建模-Use of EM Algorithm to to achieve the the the optimization of of the Gaussian mixture model, to complete the Feature Modeling
GMM
- em算法，简单实用，非常好，包括原始文档内容-em algorithm is simple and practical, very good, including the content of the original document, etc.
Gaussian-mixture-model
- 混合高斯模型GMM EM算法，建模效果好，可以用于行为识别-Gaussian mixture model GMM EM algorithm, modeling effect, can be used for behavior recognition
EM
- EM算法，迭代得到高斯混合模型对原数据进行估计.直接运行EM.m，可以直观观察运行结果-use EM algorithm to get GMM(Gaussion Mixture Model)
gmm
- EM算法以及混合高斯模型，c++实现，控制台程序，函数调用很简单方便。可以在低版本vc6.0运行。-EM algorithm and hybrid Gauss model, c++ implementation, the console program, function call is very simple and convenient. Can be run at low vc6.0.
GMM-latentSpace-v2.0
- GMM算法，利用EM算法求解混合模型中每个模型的参数-Gaussian Mixture Model,GMMalgorith,Use EM algorith
GMM
- 高斯混合模型，通过EM算法迭代得出，可用于语音识别，图像识别等各种领域(Gauss mixture model is iteratively obtained by EM algorithm, and can be used in various fields such as speech recognition and image recognition)
vbemgmm
- 在混合高斯模型参数估计方法上有很多方法，例如最大似然函数的EM算法，但是该算法容易出现过拟合，故本文提出了一个变分EM的算法来对参数进行估计，可以避免EM算法中的不足。 下面的示例文件中说明了使用下面的示例文件说明了用法 examplevbem，VBEM M示例文件 faithful.txt数据集为例(The parameters of Gauss mixture model estimation method has a lot of methods, such as the maxim
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.)
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)
EM-GMM.py
- Gaussian mixture 的 EM算法(EM algorithm for Gaussian mixture model)