Description: 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. Platform: |
Size: 1822138 |
Author:634416393@qq.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: 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: 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 Platform: |
Size: 287744 |
Author:B |
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Description: 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. Platform: |
Size: 33792 |
Author:Ming Li |
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Description: 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 Platform: |
Size: 99328 |
Author:雷雷 |
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Description: 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 Platform: |
Size: 99328 |
Author:hou |
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Description: 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. Platform: |
Size: 5120 |
Author:connie |
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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: 《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 Platform: |
Size: 8577024 |
Author:王磊 |
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Description: 《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. Platform: |
Size: 974848 |
Author:王磊 |
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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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Search - gaussian mixture models