Description: Expectation-Maximization
The EM (Expectation-Maximization) algorithm estimates the parameters of the multivariate probability density function in a form of the Gaussian mixture distribution with a specified number of mixtures.
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Author:luochentianshui@126.com |
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Description: EM算法处理高斯混和模型，是用MATLAB实现的-EM algorithm for Gaussian mixture model of treatment is achieved using MATLAB Platform: |
Size: 1024 |
Author:李晋博 |
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Description: 使用高斯模型期望值最大化演算法,做圖形分割
Gaumix_EM: EM Algorithm Applicated to Parameter Estimation for Gaussian Mixture
-Gaussian model using expectation maximization algorithm, to do graphics segmentation Gaumix_EM: EM Algorithm Applicated to Parameter Estimation for Gaussian Mixture Platform: |
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Author:李致賢 |
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Description: 统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含：
1，Analysis of linear discriminant function
2，Feature extraction: Linear Discriminant Analysis
3，Probability distribution estimation and clustering
4，Support Vector and other Kernel Machines-
This section should give the reader a quick overview of the methods implemented in
STPRtool.
• Analysis of linear discriminant function: Perceptron algorithm and multiclass
modification. Kozinec’s algorithm. Fisher Linear Discriminant. A collection
of known algorithms solving the Generalized Anderson’s Task.
• Feature extraction: Linear Discriminant Analysis. Principal Component Analysis
(PCA). Kernel PCA. Greedy Kernel PCA. Generalized Discriminant Analysis.
• Probability distribution estimation and clustering: Gaussian Mixture
Models. Expectation-Maximization algorithm. Minimax probability estimation.
K-means clustering.
• Support Vector and other Kernel Machines: Sequential Minimal Optimizer
(SMO). Matlab Optimization toolbox based algorithms. Interface to the
SVMlight software. Decomposition approaches to train the Multi-class SVM classifiers.
Multi-class BSVM formulation trained by Kozinec’s algorithm, Mitchell-
Demyanov-Molozenov algorithm Platform: |
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Author:查日东 |
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Description: 2008年3月
中国图象图形学报
基于类自适应高斯-马尔可夫随机场模型和EM 算法的MR图像分割
比较新的一片关于MARKOV以及EM算法的图像分割的文章。详细介绍了两种算法，以及对MR图像的实验结果，很有参考价值-March 2008 Journal of Image and Graphics of China based on the type of adaptive Gaussian- Markov random field model and the EM algorithm for MR image segmentation of a relatively new MARKOV as well as on the EM algorithm for image segmentation of the article. Two algorithms described in detail, as well as the experimental results of MR imaging is very useful Platform: |
Size: 268288 |
Author:luolunzi |
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Description: Free Split and Merge Expectation-Maximization algorithm for Multivariate Gaussian Mixtures. This algorithm is suitable to estimate mixture parameters and the number of conpounds-Free Split and Merge Expectation-Maximization algorithm for Multivariate Gaussian Mixtures. This algorithm is suitable to estimate mixture parameters and the number of conpounds Platform: |
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Author:ewizlab |
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Description: This package contains Matlab m-files for learning finite Gaussian mixtures from sample data and performing data classification with Mahalanobis distance or Bayesian classifiers. Each class in training set is learned individually with one of the three variations of the Expectation Maximization algorithm: the basic EM algorithm with covariance fixing, the Figueiredo-Jain clustering algorithm and the greedy EM algorithm.
The basic EM and FJ algorithms can handle complex valued data directly, the greedy EM algorithm cannot.
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Size: 20480 |
Author:李 |
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Description: 混合高斯分布中基于最大期望算法的参数估计模型，适应于通信与信号处理以及统计学领域-Mixed Gaussian distribution algorithm based on the parameters of the greatest expectations of the estimated model, adapted to communications and signal processing, as well as the field of statistics Platform: |
Size: 6144 |
Author:赵亮 |
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Description: Source code - create Gaussian Mixture Model in following steps:
1, K-means
2, Expectation-Maxximization
3, GMM
Notice: All datapoints are generated randomly and you can config in Config.h-Source code- create Gaussian Mixture Model in following steps:
1, K-means
2, Expectation-Maxximization
3, GMM
Notice: All datapoints are generated randomly and you can config in Config.h Platform: |
Size: 6144 |
Author:ChipChipKnight |
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Description: Expectation Maximization for training GMM s, diagonal covariances. Requires vqtrain.m to have a good initialization. Platform: |
Size: 1024 |
Author:Parvatishankar |
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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. Platform: |
Size: 1034240 |
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. Platform: |
Size: 6144 |
Author:ruso |
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Description: Mixture of linear regressors. The routines contained in this file allow inference and learning of a mixture of linear-Gaussian regression models. Learning is performed by maximizing the data likelihood via the expectation maximization algorithm. Platform: |
Size: 4096 |
Author:ruso |
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Description: This matlab code implements the Expectation-Maximization algorithm to estimate the parameters of a gaussian mixture model. Platform: |
Size: 12288 |
Author:kjm |
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Description: 文章展示了基于高斯混合模型的语音频谱预测方法。频谱预测可能在传包过程中预防丢包这方面起到大作用。期望最大化算法用两倍或三倍的连续语音因素来测试模型。模型被用来设计第一，儿等指令预测量。预测表用频谱分配状态来估计并和一个简单的参考模型对比。最好的预测表得到一个平均频率扭曲值是0.46dB小于参考模型-This paper presents methods for speech spectrum prediction based
on Gaussian mixture models. Spectrum prediction may be useful in a packet transmission system where the sensitivity to packet losses is a major problem.
Models of speech are trained by the Expectation Maximization algorithm using pairs, triples etc. of consecutive cepstral vectors.
The models are used to design first, second etc. order predictors.
The prediction schemes are evaluated using the spectral distortion criterion and compared to a simple reference method. The
best prediction scheme obtains an average spectral distortion that
is 0.46 dB less than for the reference method. Platform: |
Size: 296960 |
Author:will |
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Description: 基于matlab的SVM（支持向量机）算法。作为非常流行的svm工具，可以实现基于SVM的数据分析，能够应用于人工智能及模式识别领域。-Matlab based on the expectation-maximization algorithm for Gaussian mixture model (GMM) toolkit. GMM-based data can be analyzed, can be used in the field of artificial intelligence and pattern recognition. Platform: |
Size: 96256 |
Author:Zhao Sixuan |
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Description: KMEAN C#
In data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results in a partitioning of the data space into Voronoi cells.
The problem is computationally difficult (NP-hard), however there are efficient heuristic algorithms that are commonly employed and converge fast to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data, however k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes. Platform: |
Size: 2048 |
Author:Truong |
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Search - gaussian expectation maximization