Document Detail

Learning in Stochastic Bit Stream Neural Networks.
MedLine Citation:
PMID:  12662578     Owner:  NLM     Status:  Publisher    
This paper presents learning techniques for a novel feedforward stochastic neural network. The model uses stochastic weights and the "bit stream" data representation. It has a clean analysable functionality and is very attractive with its great potential to be implemented in hardware using standard digital VLSI technology. The design allows simulation at three different levels and learning techniques are described for each level. The lowest level corresponds to on-chip learning. Simulation results on three benchmark MONK's problems and handwritten digit recognition with a clean set of 500 16 x 16 pixel digits demonstrate that the new model is powerful enough for the real world applications. Copyright 1996 Elsevier Science Ltd
Max van Daalen; John Shawe-Taylor; Jieyu Zhao
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Publication Detail:
Journal Detail:
Title:  Neural networks : the official journal of the International Neural Network Society     Volume:  9     ISSN:  1879-2782     ISO Abbreviation:  Neural Netw     Publication Date:  1996 Aug 
Date Detail:
Created Date:  2003-Mar-28     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8805018     Medline TA:  Neural Netw     Country:  -    
Other Details:
Languages:  ENG     Pagination:  991-998     Citation Subset:  -    
University of London, Egham, Surrey, TW20 0EX, UK
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