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Proceedings Paper

Eliminating order dependency of classification in artificial resonance theory (ART1) networks
Author(s): Astrid Leuba; Billy V. Koen
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Paper Abstract

Incorrect classification of patterns can occur with ART1 networks when data are presented in certain sequences. The reason for this problem is the coding of the category templates, which are memory-less and give more importance to 1s than to 0s. This paper modifies the ART1 network architecture to alter these two features by adding a second set of top-down LTMs, in effect defining a second template. Computer simulations show that this modification ensures that patterns are always classified in the same category and that information is never lost. As a result, no pre-processing of the data is necessary, and ART1 networks can be used to classify patterns on-line without errors.

Paper Details

Date Published: 6 April 1995
PDF: 9 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205192
Show Author Affiliations
Astrid Leuba, Univ. of Texas/Austin (United States)
Billy V. Koen, Univ. of Texas/Austin (United States)

Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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