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Contents

PURPOSE ^

GMMBayes Toolbox - Gaussian mixture model learning and classification

SYNOPSIS ^

This is a script file.

DESCRIPTION ^

 GMMBayes Toolbox - Gaussian mixture model learning and classification
 Version 1.0

 Class learning interface
   gmmb_create          - Construct new classifier with GMM PDFs
                             Calls gmmb_em, gmmb_fj or gmmb_gem.

 Classification interface
   gmmb_pdf             - multiclass and -variate (complex) GMM PDF
   gmmb_weightprior     - multiply PDF values with constant priors
   gmmb_normalize       - scale all row sums to one
                             (useful in Bayesian classifier)
   gmmb_decide          - make the classification decisions
   gmmb_fracthresh      - threshold PDF values according to given distribution
                             density quantile, i.e., find outliers

 Legacy Classification interface
   gmmb_classify        - Run the classifier with bayesS struct.
                             (Legacy interface)

 Estimation functions
   gmmb_em              - The basic EM algorithm.
   gmmb_fj              - The Figueiredo-Jain algorithm.
   gmmb_gem             - Wrapper for the Vlassis code, Greedy EM algorithm.

 EM initializer functions
   gmmb_em_init_cmeans1 - c-means clustering, uniform weight and covariance
   gmmb_em_init_cmeans2 - c-means clustering, cluster weight and covariance
   gmmb_em_init_fcm1    - Fuzzy c-means clustering, the Toolbox v0.1 method

 Helper functions
   gmmb_cmvnpdf         - (Real and complex range) multivariate Gaussian pdf
   gmmb_covfixer        - Force matrix to a valid covariance matrix.
   gmmb_cmeans          - Simple c-means clustering
   gmmb_mkcplx          - generate complex data with Gaussian distribution
   gmmb_hist            - Create histS structure from user supplied data.
   gmmb_generatehist    - Create histS structure from generated data,
                             based on bayesS struct.
   gmmb_lhood2frac      - map PDF values to density quantiles
   gmmb_frac2lhood      - map density quantiles to PDF threshold values

 General functions
   gmmb_version         - Return version string.
   getargs              - Parse variable argument list into a struct.
   warning_wrap         - Wrapper to allow Matlab R13 style warning calls
                             in Matlab R12

 Vlassis code
   gmmbvl_demo1d
   gmmbvl_demo2d
   gmmbvl_ellipse
   gmmbvl_em
   gmmbvl_em_gauss
   gmmbvl_em_init_km
   gmmbvl_em_step
   gmmbvl_em_step_partial
   gmmbvl_kmeans
   gmmbvl_mixgen
   gmmbvl_plot2
   gmmbvl_rand_split
   gmmbvl_sqdist
   
 Demos
   gmmb_demo01          - A simple demo presenting FJ-learning and
                             Bayesian classification

 References:
   [1] Duda, R.O., Hart, P.E, Stork, D.G, Pattern Classification,
    2nd ed., John Wiley & Sons, Inc., 2001.
   [2] Bilmes, J.A., A Gentle Tutorial of the EM Algorithm and its
    Application to Parameter Estimation for Gaussian Mixture and Hidden
    Markov Models
    International Computer Science Institute, 1998
   [3] Figueiredo, M.A.T., Jain, A.K., Unsupervised Learning on
    Finite Mixture Models, IEEE transactions of pattern analysis and
    machine intelligence, vol.24, no3, March 2002
   [4] Vlassis, N., Likas, A., A Greedy EM Algorithm for Gaussian Mixture
    Learning, Neural Processing Letters 15, Kluwer Academic Publishers, 2002.
    http://carol.wins.uva.nl/~vlassis/research/learning/index_en.html
   [5] Paalanen, P., Kamarainen, J.-K., Ilonen, J., Kälviäinen, H.,
    Feature Representation and Discrimination Based on Gaussian Mixture Model
    Probability Densities - Practices and Algorithms, Research Report 95,
    Lappeenranta University of Technology, Department of Information
    Technology, 2005.

 Authors:
    Joni Kamarainen <Joni.Kamarainen@lut.fi>
    Pekka Paalanen <pekka.paalanen@lut.fi>

 Acknowledgements:
    All the gmmbvl_* functions and files are not written by the authors of
    the Toolbox.
    They are written by Dr. Nikos Vlassis and Sjaak Verbeek
    unless otherwise noted in each file and are intellectual property of
    their writers. The code is included "as is" [4], except the file and
    function names are changed.

    The Figueiredo-Jain algorithm implementation is partly based
    on the code published in http://www.lx.it.pt/~mtf/

 Copyright:

   The GMMBayes Toolbox is Copyright (C) 2003, 2004
   by Joni Kamarainen and Pekka Paalanen except
   the gmmbvl_*.m files which are copyrighted by their
   respective authors.

   The software package is free software; you can redistribute it
   and/or modify it under terms of GNU General Public License as
   published by the Free Software Foundation; either version 2 of
   the license, or any later version. For more details see licenses
   at http://www.gnu.org

   The software package is distributed in the hope that it will be useful,
   but WITHOUT ANY WARRANTY; without even the implied warranty of
   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
   See the GNU General Public License for more details.

   As stated in the GNU General Public License it is not possible to
   include this software library or even parts of it in a proprietary
   program without written permission from the owners of the copyright.
   If you wish to obtain such permission, you can reach us by mail:

      Department of Information Processing
      Lappeenranta University of Technology
      P.O. Box 20 FIN-53851 Lappeenranta
      FINLAND

  and by e-mail:
      
      joni.kamarainen@lut.fi
      pekka.paalanen@lut.fi

  Please, if you find any bugs contact authors.

  Project home page: http://www.it.lut.fi/project/gmmbayes/

   $Name:  $ $Revision: 1.4 $  $Date: 2005/04/14 10:33:34 $

CROSS-REFERENCE INFORMATION ^

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