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Description: 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 estimates GMM Matlab version of the source code, suitable for the type of machine learning problems
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Author: 罗军 |
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Description: 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 estimates GMM Matlab version of the source code, suitable for the type of machine learning problems
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Size: 3072 |
Author: 罗军 |
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Description: 这是一个gmm的库，对学习gmm非常有用，希望大家多交流-This is a library gmm, gmm of learning is very useful, I hope all parties to have more exchanges
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Size: 199680 |
Author: 郑堃 |
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Description: In statistics, a mixture model is a probabilistic model for density estimation using a mixture distribution. A mixture model can be regarded as a type of unsupervised learning or clustering. Mixture models should not be confused with models for compositional data, i.e., data whose components are constrained to sum to a constant value (1, 100 , etc.).
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Size: 40960 |
Author: alice |
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Description: GMM-GMR is a set of Matlab functions to train a Gaussian Mixture Model (GMM) and retrieve generalized data through Gaussian Mixture Regression (GMR). It allows to encode efficiently any dataset in Gaussian Mixture Model (GMM) through the use of an Expectation-Maximization (EM) iterative learning algorithms. By using this model, Gaussian Mixture Regression (GMR) can then be used to retrieve partial output data by specifying the desired inputs. It then acts as a generalization process that computes conditional probability with respect to partially observed data.
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Author: ning |
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Description: Bayesian mixture of Gaussians. This set of files contains functions for performing inference and learning on a Bayesian Gaussian mixture model. Learning is carried out via the variational expectation maximization algorithm.
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Size: 6144 |
Author: ruso |
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Description: 本程序为EMD-HHT-M源代码，供大家交流学习，语音识别专用代码-This program is EMD-HHT-M source code for all to share learning, speech recognition-specific code
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Author: wrd |
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Description: Speaker recognition is the task of validating individual s identity using invariant features extracted from their voices print. Speaker recognition technology common applications include authentication, surveillance and forensic applications. This Paper investigates the performance of three automatic model selections based on Gaussian Mixture Model (GMM). These approaches are Bayesian information criterion (BIC), Bayesian Ying–Yang harmony empirical learning criterion (BYY-HEC) and Bayesian Ying–Yang harmony data smoothing learning criterion (BYY-HDS). Experimental evaluation of these methods is presented.
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Author: ZCEEE |
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Description: Em algo for GMM, data mining unsupervised learning-Em algo for GMM, data mining unsupervised learning
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Size: 2048 |
Author: vicky |
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Description: sparse Bayesain learning algorithms
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Size: 19456 |
Author: lucasschen |
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Description: 无监督混合高斯模型（GMM）的EM估计，含两篇IEEE论文的源码-This is a set of MATLAB m-files implementing the mixture
fitting algorithm described in the paper
M. Figueiredo and A.K.Jain, "Unsupervised learning of
finite mixture models", IEEE Transaction on Pattern Analysis
and Machine Intelligence, vol. 24, no. 3, pp. 381-396, March 2002.
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Size: 24576 |
Author: 郑馨 |
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Description: an incremental learning process of Gaussian Mixture Model (GMM)
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Size: 19456 |
Author: Amir |
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Description: 机器学习源代码：内含多种机器学习算法，如GMM、KNN、Kernel PCA等等，供参考。-Machine learning source code: contains a variety of machine learning algorithms, such as GMM, KNN, Kernel PCA for reference.
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Size: 4093952 |
Author: 胡龙 |
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Description: 高斯混合模型，对单高斯和混合高斯模型进行介绍与学习-Gaussian mixture model, the single-Gaussian and Gaussian mixture model describes learning
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Size: 138240 |
Author: wang |
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Description: 基于混合高斯的运动目标提取，附带图像序列，内容讲解详细，适合学习。-Extracted based on Gaussian mixture moving target, with a sequence of images, to explain in detail the contents of, suitable for learning.
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Size: 1658880 |
Author: 水清河 |
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Description: 不错的GM_EM代码。用于聚类分析等方面。- GM_EM- fit a Gaussian mixture model to N points located in n-dimensional
space.
Note: This function requires the Statistical Toolbox and, if you wish to
plot (for k = 2), the function error_ellipse
Elementary usage:
GM_EM(X,k)- fit a GMM to X, where X is N x n and k is the number of
clusters. Algorithm follows steps outlined in Bishop
(2009) Pattern Recognition and Machine Learning , Chapter 9.
Additional inputs:
bn_noise- allow for uniform background noise term ( T or F ,
default T ). If T , relevant classification uses the
(k+1)th cluster
reps- number of repetitions with different initial conditions
(default = 10). Note: only the best fit (in a likelihood sense) is
returned.
max_iters- maximum iteration number for EM algorithm (default = 100)
tol- tolerance value (default = 0.01)
Outputs
idx- classification/labelling of data in X
mu- GM centres
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Author: 朱魏 |
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Description: 混合高斯模型使用K（基本为3到5个） 个高斯模型来表征图像中各个像素点的特征,在新一帧图像获得后更新混合高斯模型,用当前图像中的每个像素点与混合高斯模型匹配,如果成功则判定该点为背景点, 否则为前景点。通观整个高斯模型，他主要是有方差和均值两个参数决定，,对均值和方差的学习,采取不同的学习机制,将直接影响到模型的稳定性、精确性和收敛性。由于我们是对运动目标的背景提取建模，因此需要对高斯模型中方差和均值两个参数实时更新。为提高模型的学习能力,改进方法对均值和方差的更新采用不同的学习率 为提高在繁忙的场景下,大而慢的运动目标的检测效果,引入权值均值的概念,建立背景图像并实时更新,然后结合权值、权值均值和背景图像对像素点进行前景和背景的分类。-Gaussian mixture model using K (essentially 3-5) Gaussian model to characterize the features of each pixel in the image, in the image of the new frame for updated Gaussian mixture model, with each pixel in the image with a Gaussian mixture current model matching, if successful, determined that the point of the background points, otherwise the former attraction. Throughout the entire Gaussian model, he mainly has two parameters determine the variance and the mean, the mean and variance of the study, to take a different learning mechanism, will directly affect the stability, accuracy and convergence model. Since we are moving object extraction of the background modeling, so the need for the Gaussian model variance and mean two parameters real-time updates. In order to improve the learning ability of the model, an improved method for updating the mean and variance of different learning rates to improve in the busy scene, large and slow moving object detection results, the introduction of
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Author: 尹安然 |
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Description: 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 clustering, but also often used for density estimation).
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Size: 19456 |
Author: 赵小娟 |
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Description: 高斯混合模型GMM的学习资料，包含一个学习笔记和一个简单的C++实现-Gaussian mixture model learning materials, including notes and a simple C++ source code
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Size: 2398208 |
Author: 公子寒 |
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Description: GMM高斯混合模型EM算法聚类，PCA主成分分析，以及从人脸图像中提取主成分(GMM Gauss hybrid model EM algorithm clustering, PCA principal component analysis, and extraction of principal components from face images)
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Author: linann
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