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: 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: 一个在matlab环境下编写的采用expectation maximization方法计算高斯混合模型的程序。-One in the matlab environment prepared by the use of expectation maximization method GMM procedure. Platform: |
Size: 1024 |
Author:windman |
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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: duke的tutorial on EM的matlab经典源码，值得一看。-Matlab code for the tutorial on Expectation Maximization，worth a visit. Platform: |
Size: 8192 |
Author:yujin liu |
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Description: EM算法介绍及Matlab演示代码(一维和多维高斯混合模型学习算法)-Introduction of EM algorithm and Matlab codes that implement the algorithm Platform: |
Size: 203776 |
Author:冰激凌 |
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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: I present an expectation-maximization (EM) algorithm for principal
component analysis (PCA). Platform: |
Size: 121856 |
Author:sreenath |
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Description: 关于最大似然重建方法的实现，可用于tomography reconstruction-This is the code for maximum likelihood expectation maximum reconstruction method which is frequently applied in tomography reconstruction, such as CT and PET Platform: |
Size: 1024 |
Author:Xiubin Dai |
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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: 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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