Description: The EM algorithm is short for Expectation-Maximization algorithm. It is based on an iterative optimization of the centers and widths of the kernels. The aim is to optimize the likelihood that the given data points are generated by a mixture of Gaussians. The numbers next to the Gaussians give the relative importance (amplitude) of each component.-The EM algorithm is short for Expectation- Maximization algorithm. It is based on an ITERA tive optimization of the centers and widths of t he kernels. The aim is to optimize the likelihoo d that the given data points are generated by a mi xture of Gaussians. The numbers next to the Gaus sians give the relative importance (amplitude ) of each component. Platform: |
Size: 15360 |
Author:陈伟 |
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Description: EM算法，基于期望最大化原则进行密度估计-EM algorithm, based on the expectation maximization of the principle of density estimation Platform: |
Size: 3072 |
Author:丁宏锴 |
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Description: 期望极大化（EM）算法及其应用，对从事统计研究的人比较游泳。-Expectation maximization (EM) algorithm and its application, to the conduct of statistical research compared to swimming. Platform: |
Size: 93184 |
Author:胡兵 |
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Description: 用matlab语言写的EM(Expectation maximization)算法,用于模式分类-Matlab language used to write the EM (Expectation maximization) algorithm for pattern classification Platform: |
Size: 2048 |
Author:罗升阳 |
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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: |
Size: 1024 |
Author:李致賢 |
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Description: 非常好的EM算法介绍,不妨去看看.The Expectation Maximization Algorithm
A short tutorial-Very good introduction EM algorithm, may wish to go and see. The Expectation Maximization AlgorithmA short tutorial Platform: |
Size: 82944 |
Author:stephen |
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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: |
Size: 4271104 |
Author:查日东 |
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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: |
Size: 220160 |
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.
Platform: |
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: It actually simulates the registration process of multiple dissimilar sensors in a wireless sensor network using the expectation maximization algorithm. Platform: |
Size: 2048 |
Author:papa_roach |
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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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