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Unsupervised Learning of a Finite Gamma Mixture Using MML: Application to SAR Image Analysis
Cambridge UK August 23-August 26
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.133404217th International Conference on Patt ...
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Djemel Ziou, Nizar Bouguila, "Unsupervised Learning of a Finite Gamma Mixture Using MML: Application to SAR Image Analysis," Pattern Recognition, International Conference on, vol. 2, pp. 68-71, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 2, 2004.
 
 
BibTex
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@article{ 10.1109/ICPR.2004.1334042,
author = {Djemel Ziou and Nizar Bouguila},
title = {Unsupervised Learning of a Finite Gamma Mixture Using MML: Application to SAR Image Analysis},
journal ={Pattern Recognition, International Conference on},
volume = {2},
year = {2004},
issn = {1051-4651},
pages = {68-71},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.1334042},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Pattern Recognition, International Conference on
TI - Unsupervised Learning of a Finite Gamma Mixture Using MML: Application to SAR Image Analysis
SN - 1051-4651
SP68
EP71
A1 - Djemel Ziou,
A1 - Nizar Bouguila,
PY - 2004
KW - null
VL - 2
JA - Pattern Recognition, International Conference on
ER -
 
Djemel Ziou, Universit? de Sherbrooke, Canada
Nizar Bouguila, Universit? de Sherbrooke, Canada
This paper discusses the unsupervised learning problem for a mixture of Gamma distributions. An important part of the unsupervised problem is determining the number of components which best describes some data. We apply the Minimum Message Length (MML) criterion to the unsupervised learning problem in the case of a mixture of Gamma distributions. We give a comparison of criteria in the literature for estimating the number of components in a data set. The comparison concerns synthetic and RADARSAT SAR images.
Citation:
Djemel Ziou, Nizar Bouguila, "Unsupervised Learning of a Finite Gamma Mixture Using MML: Application to SAR Image Analysis," icpr, vol. 2, pp.68-71, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 2, 2004
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