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.
- [GreedyEM] - greedy em hybrid model training algorith
- [Matlaboperations.Rar] - a very good operation, including linear
- [gendata] - For Pattern Recognition MATLAB source co
- [Gaumix_EM] - Gaussian model using expectation maximiz
- [dd_tools] - ddtool, the realization of one class cla
- [randomwalk] - random walk for solution attachment， dev
- [rnnsimv2] - em algorithm source code in matlab
- [GreedyRandomizedSearch] - Matlab Implementation of Greedy Randomiz
- [cseg] - image color segmentation using Euclidean
- [emgmm] - Maximum likelihood estimation of Gaussia
File list (Check if you may need any files):
111186771gmmbayestb-v0.1.tar
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