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Proceedings Paper

Pattern classifier: an alternative method of unsupervised learning
Author(s): Atilla Ekrem Gunhan
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Paper Abstract

In the present work, an alternative multi-layer unsupervised neural network model that may approximate certain neurophysiological features of natural neural systems has been studied. The network is formed by two parts. The first part of the network plays a role as a short term memory that is a temporary storage for each pattern. The task for this part of the network is to preprocess incoming patterns without memorizing, in other words, to reduce the linear dependency among patterns by determining their relevant representations. This preprocessing ability is obtained by a dynamic lateral inhibition mechanism on the hidden layer. These representations are the input patterns for the next part of the network. The second part of the network may be accepted as a long term memory which classifies and memorizes incoming pattern informations that come from a hidden layer. As long as the hidden layer has preprocessed pattern information, the final classification and memorizing process is easy.

Paper Details

Date Published: 1 July 1992
PDF: 12 pages
Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140151
Show Author Affiliations
Atilla Ekrem Gunhan, Univ. of Bergen (Norway)


Published in SPIE Proceedings Vol. 1710:
Science of Artificial Neural Networks
Dennis W. Ruck, Editor(s)

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