搜索资源列表
80957954GMM
- 【highspeedlogic】matlab代做，基于matlab的GMM视频跟踪算法的仿真-[matlab] highspeedlogic generation to do, based on the GMM matlab video tracking algorithm simulation
gmmMatlab
- descreption of GMM in matlab with code source
GMMmatlabD
- Discr iption of GMM MATLAB WITH CODE SOURCE
GMM_gulici
- 基于GMM的孤立词识别，包含源代码和语料-isolated word recognition based on GMM, including source code and the corpus
GMMbkg
- 采用GMM模型进行视频背景提取，根据opencv的函数改写的-GMM background extraction
test_vioce
- 基于MFCC的GMM语音识别,该算法清晰明了，简单易懂，非常的适合初学者。-mfcc gmm
GMM_KDE
- malab代做|hslogic|基于MATLAB的GMM和KDE核估计得目标跟踪仿真-Malab generation to do |hslogic| based MATLAB GMM and KDE kernel estimation target tracking simulation
Gaussian-Mixture-Model-GMM
- guassian mixture model matlab code
gmmtrain_EM
- GMM UBM TRAINING CODE
MachineLearning-master
- 一些常见的机器学习算法的实现代码。包括PCA、kNN、logistic regression、ManifoldLearning、SVM、GMM等-Some common machine learning algorithm implementation code. Including PCA, kNN, logistic regression, ManifoldLearning, SVM, GMM, etc.
matlab-gmm
- tracking cars by Gaussian Mixture Model
52957362
- EM算法估计GMM的matlab版本的源代码,适合给类机器学习问题,-EM algorithm to estimate GMM version of the matlab source code, suitable for such machine learning problems,
mutlabalgorithmnot
- The mm algorithm to solve the problem of GMM parameters, matlab source version, not encrypted,
98868527
- 关于高斯混合模型GMM的matlab源代码,不错的源码-About gaussian mixture model of GMM matlab source code, source code
python
- 简单的gmm聚类demo。 简单的gmm聚类demo。- U7B80 u5355 u7684gmm u808A u802A u7C2B u2002
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)
背景差GMM
- opencv，vs2010 利用混合高斯模型，得到运动前景，与静态背景(Opencv and VS2010 use hybrid Gauss model to obtain motion foreground and static background)
speaker-recognition-master
- example of speech processing to execute
em_ghmm
- gussion mix model to model the speech
em
- em算法介绍:EM算法有很多的应用，最广泛的就是GMM混合高斯模型、聚类、HMM等等(This is the EM algorithm using JAVA, easy to understand, easy to use and helpful for understanding the EM algorithm)