搜索资源列表
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
- 用java实现混合高斯模型，做特征分类，模式识别等应用(The hybrid Gauss model is implemented by Java, and the feature classification and pattern recognition are performed)
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)
92920914
- EM algorithm to estimate GMM version of the matlab source code, suitable for such machine learning problems,
623981
- The mm algorithm to solve the problem of GMM parameters, matlab source version, not encrypted,
example2
- data for gmm testing
RZLNB33
- 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)
GMM
- 混合高斯算法，用opencv3实现，用于对运动目标进行检测 效果还算是不错的，代码通俗易(Hybrid Gauss algorithm)
高斯混合模型GMM-latentSpace-v2.0
- 用于背景建模实现视频运动目标分割 与目标跟踪算法(For background modeling, video moving object segmentation and object tracking algorithm)
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
- 该程序可实现对监控视频的有效前景提取，非常稳定(Video foreground extraction)
Guess
- 用m语言绘出高斯分布，并以GMM模型的简单例子来进行非曲线拟合。(The Gauss distribution is drawn in M language, and a simple example of the GMM model is used to do the non curve fitting.)
Speech Processing Analysis - MATLAB
- The number of states in GMM as the generative model of the frames is obtained using k-means algorithm. This also helps to initialize the mean vector and the covariance matrix of the individual state of the GMM. The training LPC frames collected fro
GMM_EM
- 混合高斯模型的参数计算方法，采用EM迭代的方法求得(Parameter calculation method of mixed Gauss model)
DanGS
- 用于运动目标检测，对于高架路啊，可检测出运动车辆(Moving object detection)