DocumentCode :
3850268
Title :
Gamma-filter self-organising neural networks for unsupervised sequence processing
Author :
P.A. Estevez;R. Hernandez;C.A. Perez;C.M. Held
Author_Institution :
Department of Electrical Engineering and Advanced Mining Technology Center, Universidad de Chile
Volume :
47
Issue :
8
fYear :
2011
fDate :
4/14/2011 12:00:00 AM
Firstpage :
494
Lastpage :
496
Abstract :
Adding γ-filters to self-organising neural networks for unsupervised sequence processing is proposed. The proposed γ-context model is applied to self-organising maps and neural gas networks. The γ-context model is a generalisation that includes as a particular example the previously published merge-context model. The results show that the γ-context model outperforms the merge-context model in terms of temporal quantisation error and state-space representation.
Journal_Title :
Electronics Letters
Publisher :
iet
ISSN :
0013-5194
Type :
jour
DOI :
10.1049/el.2011.0115
Filename :
5751790
Link To Document :
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