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DESIGN_AND_IMPLEMENT_A_SYSTEM_oF_GRASP_IDENTIFICAT
- 机器人灵巧手的抓持分类是抓持规划的一个主要问题．本文应用模式识别技术设计和实现了一种基于高斯混合模型GMM 的分类器．采用Expectation Maximization(EM)算法估计GMM 的参数，对人手的抓持动作进行识别与分类，经过人手到机器人手的关节空间运动映射。
111186761EMnormmixtest.rar
- 用matlab实现高斯混合模型Em算法的源程序哪位有呀,帮帮忙啦- 工具箱与,With the realization of Gaussian mixture model matlab algorithm Em Which source are you? Help help you- the toolbox and
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
xml_toolbox-3.2.1-matlab-7.0-R14
- 高斯混合模型的matlab的实现，但是不包含em算法在里面，但是有版权，注意阅读内容-Gaussian mixture model of the matlab implementation, but does not include the em algorithm inside, but has the copyright and pay attention to read the content
Em
- 通过em算法实现对数据的高斯混合模型的分类-Em algorithm through implementation of data Gaussian mixture model classification
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
em
- MATLAB实现的EM算法，用于高斯混合模型的均值、方差、权重估计-EM algorithm based on MATLAB
em-algorithm-for-gmm
- 基于高斯混合模型(GMM)的em算法，给出了其matlab实现-em algorithm for GMM
emgm
- EM算法用于高斯混合模型，实现数据的精确分类-The EM algorithm for Gaussian mixture model, the exact classification of the data
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
em(1)
- EM算法实现高斯混合模型参数估计的matlab程序，可以下载学习。-EM algorithm to achieve the matlab program Gaussian mixture model parameter estimation, you can download the study.
EM
- 实现EM算法的MATLAB仿真程序，利用高斯混合模型实现EM聚类算法，并比较估计参数。-EM algorithm to achieve the MATLAB simulation program, using Gaussian Mixture Model EM clustering algorithm, and compare the estimated parameters.
EM_GMM
- 基于EM算法实现的高斯混合模型数据分类，可以很优秀的对各种数据进行聚类分析，R语言实现-EM algorithm based on Gaussian mixture model data classification, can be very good for a variety of data clustering analysis, R language
gmeem
- 程序基于EM算法实现多维高斯混合模型的参数估计。-Parameter estimation of multi dimensional Gauss mixture model based on EM algorithm.
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_EM
- GMM算法是混合高斯模型，其求解过程需要不断迭代，本程序利用EM算法进行了仿真实现，可以加深对GMM的理解。(GMM algorithm is a hybrid Gauss model, and its solution process needs iteration. This program uses EM algorithm for simulation, which can deepen the understanding of GMM.)
EMfc
- 一个EM算法 这是通过高斯混合模型进行聚类的EM算法的实现，使用图形在线表示。(EM algorithm This is an implementation of the EM algorithm for Clustering via Gaussian mixture models, using graphical on-line representation.)
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.)