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Description: 用C＋＋实现的高斯混合模型的算法类，方差矩阵是对角矩阵-C++ Gaussian mixture model algorithm category, variance matrix is diagonal matrix
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Size: 4096 |
Author: 方平 |
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Description: 这个源码提供了如何利用高斯混合模型去做影像辨识的模型分类-the source how to use a Gaussian mixture model to do imaging Identification Classification Model
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Size: 15360 |
Author: 小王 |
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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.
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Size: 15360 |
Author: 陈伟 |
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Description: 采用期望最大算法最优化初始高斯混合模型的程序，笔者借用ubm自适应方法，很好的解决了模型不收敛的问题。-expectations largest algorithm using optimization initial Gaussian mixture model procedures, the author borrowed ubm adaptive method, a good model is not the solution convergence problem.
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Size: 4096 |
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Description: 关于高斯混合模型（GMM）的matlab源代码-on Gaussian mixture model (GMM) Matlab source code
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Size: 11264 |
Author: 冯君 |
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Description: Gaussian Mixture Utilities
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Size: 27648 |
Author: sunxiaodian |
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Description: 机器学习中的E M算法，本代码是基于高斯混合模型的E M 算法聚类。-machine learning algorithm E M, the code is based on the Gaussian mixture model clustering algorithm E. M.
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Size: 4096 |
Author: 李民 |
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Description: 高斯混合模型，一个非常有用的图像处理方法-Gaussian mixture model, a very useful image processing method
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Size: 2125824 |
Author: 张文国 |
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Description: Fit a multivariate gaussian mixture by a cross-entropy method. Cross-Entropy is a powerfull tool to achieve stochastic multi-extremum optimization.
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Size: 387072 |
Author: 阳关 |
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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
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Size: 1024 |
Author: 李晋博 |
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Description: gaussian mixture modelling in matlab
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Size: 56320 |
Author: paul huang |
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Description: EM算法是机器学习领域中常用的一种算法，这个文件是EM算法最简单的一种实现，即在Gaussian Mixture model上面的EM。-EM field of machine learning algorithm is commonly used in an algorithm, this document is the most simple EM algorithm as a realization that, in Gaussian Mixture model above EM.
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Size: 3072 |
Author: De-Chuan Zhan |
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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
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Size: 1024 |
Author: 李致賢 |
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Description: 这是一个高斯混合模型的源代码,环境是matlab,
高斯混合模型运用于多个方面,在信号处理中应用非常广泛,初学者可以作为参考-This is a Gaussian mixture model of the source code, the environment is matlab, Gaussian mixture model applied to various aspects of signal processing applications in a very wide range of beginners can be used as reference
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Size: 3072 |
Author: 亿摆 |
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Description: Creates a Gaussian mixture model with specified architecture.MIX = GMM(DIM, NCENTRES, COVARTYPE) takes the dimension of the space
DIM, the number of centres in the mixture model and the type of the
mixture model, and returns a data structure MIX.
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Size: 2048 |
Author: 西晃云 |
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Description: 高斯混合模型的matlab源代码，拭一款经典的程序-Gaussian mixture model matlab source code, taking a classic procedure
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Size: 57344 |
Author: 林枫 |
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Description: ％ EM algorithm for k multidimensional Gaussian mixture estimation
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％ Inputs:
％ X(n,d) - input data, n=number of observations, d=dimension of variable
％ k - maximum number of Gaussian components allowed
％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none)
％ maxiter - maximum number of iteration allowed ([] for none)
％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none)
％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none)
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％ Ouputs:
％ W(1,k) - estimated weights of GM
％ M(d,k) - estimated mean vectors of GM
％ V(d,d,k) - estimated covariance matrices of GM
％ L - log likelihood of estimates
％- EM algorithm for k multidimensional Gaussian mixture estimation Inputs: X (n, d)- input data, n = number of observations, d = dimension of variable k- maximum number of Gaussian components allowed ltol- percentage of the log likelihood difference between 2 iterations ([] for none) maxiter- maximum number of iteration allowed ([] for none) pflag- 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) Init- structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) Ouputs: W (1, k)- estimated weights of GM M (d, k)- estimated mean vectors of GM V (d, d, k)- estimated covariance matrices of GM L- log likelihood of estimates
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Size: 3072 |
Author: Shaoqing Yu |
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Description: GMMBayes Toolbox
Feature Representation and Discrimination Based on Gaussian Mixture Model
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Size: 87040 |
Author: fgqqd |
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Description: 基于Matlab的高斯混合模型的算法实现，该程序可以实现对图像处理的功能-Matlab based on the Gaussian mixture model algorithm, the program can achieve the functions of image processing
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Size: 5120 |
Author: geyu |
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Description: EM算法计算混合高斯模型，可以计算三个参数。-Gaussian mixture model EM algorithm, three parameters can be calculated.
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Size: 9216 |
Author: 袁宇 |
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