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Hwang, W.J. and Ye, B.Y. and Lin, C.T.
ABSTRACT
A competitive learning algorithm for the parametric classification of Gaussian sources is presented in this letter. The algorithm iteratively estimates the mean and prior probability of each class during the training. Bayes rule is then used for classification based on the estimated information. Simulation results show that the proposed algorithm outperforms k-means and LVQ algorithms for the parametric classification. 
ECVision indexed and annotated bibliography of cognitive computer vision publications
This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1
The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
A novel competitive learning algorithm for the parametric classification with Gaussian distributionsSite generated on Friday, 06 January 2006