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A general framework for solving image inverse problems is introduced in this paper. The approach is based on Gaussian mixture models, estimated via a computationally efficient MAP-EM algorithm.
Date : 2011-04-27 Size : 1.74mb User : 634416393@qq.com

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
Date : 2025-07-03 Size : 1kb User : 李晋博

GMM Gaussian Mixture Models in C
Date : 2025-07-03 Size : 268kb User : paulo

a document about "Baseball Playfield Segmentation Using Adaptive Gaussian Mixture Models"
Date : 2025-07-03 Size : 725kb User : mkhaled

DL : 0
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
Date : 2025-07-03 Size : 4.07mb User : 查日东

very good Gaussian Mixture Models and Probabilistic Decision-Based Neural Networks for Pattern Classification - A Comparative Study document -very good Gaussian Mixture Models and Probabilistic Decision-Based Neural Networks for Pattern Classification- A Comparative Study document
Date : 2025-07-03 Size : 281kb User : B

用高斯混合模型进行数据聚类分析的matlab 程序。-Set of files for analysis of Gaussian mixture models for data set clustering etc.
Date : 2025-07-03 Size : 33kb User : Ming Li

EM算法(英文)A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models-A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models
Date : 2025-07-03 Size : 97kb User : 雷雷

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EM算法简明教程 用于高斯分布隐马尔可夫模型的参数估计-Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models
Date : 2025-07-03 Size : 97kb User : hou

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3 books about Gaussian Mixture Models Detail: CLustering with GMMs Distance between GMMs Initialize G-3 books about Gaussian Mixture Models Detail: CLustering with GMMs Distance between GMMs Initialize GMM
Date : 2025-07-03 Size : 768kb User : ChipChipKnight

Gaussian Mixture Models (GMM) for speech noise reduction
Date : 2025-07-03 Size : 252kb User : eddy

利用混合高斯模型进行前景检测的源代码实现,依据的是Stauffer发表的Adapptive background mixture models for real-time tracking.-The prospects for the use of Gaussian mixture model, detection of the source code implementation, based on the Stauffer published Adapptive background mixture models for real-time tracking.
Date : 2025-07-03 Size : 5kb User : connie

GMM Model Gaussian Mixture Models - Algorithm and Matlab Code-GMM Model!
Date : 2025-07-03 Size : 42kb User : Kendall Wang

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Player Detection Algorithm Based on Gaussian Mixture Models Background Modeling
Date : 2025-07-03 Size : 255kb User : reza

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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.
Date : 2025-07-03 Size : 4kb User : ruso

《数值分析方法库_第三版 》本书选材内容丰富,包括了当代科学计算过程中涉及的大量内容:求特殊函数值、随机数、排序、最优化、快速傅里叶变换、谱分析、小波变换、统计描述和数据建模、偏微分议程数值解、若乾编码算法和任意精度计算等。本书科学性和实用性统一,不仅对每种算法进行了数学分析和比较,而且根据作者经验对算法给出了评论和建议,并在此基础上提供了用C++语言编写的实用程序。 -《Numerical Recipes 3rd Edition: The Art of Scientific Computing》, Do you want easy access to the latest methods in scientific computing? This greatly expanded third edition of Numerical Recipes has it, with wider coverage than ever before, many new, expanded and updated sections, and two completely new chapters. The executable C++ code, now printed in color for easy reading, adopts an object-oriented style particularly suited to scientific applications. Co-authored by four leading scientists from academia and industry, Numerical Recipes starts with basic mathematics and computer science and proceeds to complete, working routines. The whole book is presented in the informal, easy-to-read style that made earlier editions so popular. Highlights of the new material include: a new chapter on classification and inference, Gaussian mixture models, HMMs, hierarchical clustering, and SVMs a new chapter on computational geometry, covering KD trees, quad- and octrees, Delaunay triangulation, and
Date : 2025-07-03 Size : 8.18mb User : 王磊

《Software for Flexible Bayesian Modeling and Markov Chain Sampling》是机器学习领域专家Neal编写的用于Bayesian和马尔可夫链Linux下的C语言工具包。很有名,也很权威。 -This software supports Bayesian regression and classification models based on neural networks and Gaussian processes, and Bayesian density estimation and clustering using mixture models and Dirichlet diffusion trees. It also supports a variety of Markov chain sampling methods, which may be applied to distributions specified by simple formulas, including simple Bayesian models defined by formulas for the prior and likelihood.
Date : 2025-07-03 Size : 952kb User : 王磊

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用Matlab写的高斯混合模型的贝叶斯EM估计,-Variational Bayesian EM for Gaussian mixture models
Date : 2025-07-03 Size : 22kb User : cheng

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.
Date : 2025-07-03 Size : 290kb User : will

Gaussian Mixture Models
Date : 2025-07-03 Size : 44kb User : hassan
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