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M. R. Silvestre and L. L. Ling
ABSTRACT
In this article we describe a feature extraction algorithm for pattern classification based on Bayesian decision boundaries and pruning techniques. The proposed method is capable of optimizing MLP neural classifiers by retaining those neurons in the hidden layer that really contribute to correct classification. Also, we proposed a method which defines a plausible number of neurons in the hidden layer based on the stem-and-leaf graphics of training samples. Experimental investigation reveals the efficiency of the proposed method. 
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
Optimization of neural classifiers based on Bayesian decision boundaries and idle neurons pruningSite generated on Friday, 06 January 2006