搜索资源列表
GMM
- 聚类算法之高斯混合模型,GMM 和 k-means 很像,不过 GMM 是学习出一些概率密度函数来(所以 GMM 除了用在 clustering 上之外,还经常被用于 density estimation )。-Gaussian mixture model of clustering algorithm, GMM and k-means like, but GMM is learning some probability density function (so GMM except on cl
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.
GMMmatlab
- GMM算法用于多个正太分布参数估计-GMM designed for more than one arguments gussing and so..............
GMM-Kmeans
- GMM及Kmeans算法实现,包含简单的测试程序,可直接在Linux下编译-GMM and Kmeans algorithm to achieve, including a simple test procedures, can be directly compiled under the Linux
GMM
- 高斯混合模型算法流程,一组测试数据进行分类,用osg显示-Gaussian mixture model algorithm process, a set of test data classification, using osg display
GMM
- 这个混合高斯目标检测算法的c++代码,并对一组序列进行检测。-This hybrid Gauss target detection algorithm c++ code, and a set of sequences to detect.
GMM
- 聚类算法的方法,用网上的代码,运行完了使用的-Clustering algorithm, using the code on the Internet, run over the use of the
GMM
- gmm_em算法,可用于手写数字识别,供初学者参考。-Gmm_em algorithm and can be used for handwritten numeral recognition, for beginners reference.
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)
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)
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
gmmreg_cpd
- GMM模型的CPD配准算法,非刚性配准,点集配准(CPD registration algorithm of GMM model)
GMM_EM
- GMM模型下的EM算法,一个实用的matlab仿真代码(EM algorithm under GMM model)
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)
sml
- 使用某类图像作为训练样本,基于SML算法的类模型,(Class model based on SML algorithm)
EM-GMM.py
- Gaussian mixture 的 EM算法(EM algorithm for Gaussian mixture model)
HMM1
- 在VC6.0平台上进行编写的,包括隐马尔科夫模型(HMM)和混合高斯模型(GMM)在内的用于模板训练的算法。(The algorithm for template training is written on VC6.0 platform, including hidden Markov model (HMM) and mixed Gauss model (GMM).)
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 algorithm in detail.)
automatic_image_segement
- 本文以k-means算法为背景,引入信息熵相关知识,从而实现全自动分割图像。然而在利用混合高斯模型对图像进行数据分析时,会发生一定的过拟合现象,导致我们得不到预期的聚类数目。本文设计合理的合并准则,令模型简化,有效地消除过拟合现象,使得最后得到的聚类数目与预期符合。,设计合理的准则改进了图像的全自动分割方法,使得分割结果更加优化(In this paper, k-means algorithm is used as the background, and information entropy