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G. Ou and Y. L. Murphey and L. Feldkamp
ABSTRACT
Multiclass neural learning involves finding appropriate neural network architecture, encoding schemes, learning algorithms, etc. We discuss major approaches used in neural networks for classifying multiple classes. The discussion is focused on these architectures using either a system of multiple neural networks or a single neural network. We discuss various learning algorithms, one-again-all, one-against-one, and p-against-q. We also discuss training procedures associated with each approach, implementation and time complexity. These methods are evaluated through their performances on the NlST handwritten digit database. 
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
Multiclass pattern classification using neural networksSite generated on Friday, 06 January 2006