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Proceedings Paper

Survey of learning results in adaptive resonance theory (ART) architectures
Author(s): Michael Georgiopoulos; J. Huang; Gregory L. Heileman
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Paper Abstract

In this paper we investigate the learning properties of ART1, Fuzzy Art, and ARTMAP architectures. These architectures were introduced by Carpenter and Grossberg over the last eight years. some of the learning properties discussed in this paper involve characteristics of the clusters formed in these architectures while other learning properties concentrate on how fast it will take these architectures to converge to a solution for the type of problems that are capable of solving. This latter issue is very important in the neural network literature, and there are very few instances where it has been answered satisfactorily.

Paper Details

Date Published: 6 April 1995
PDF: 9 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205147
Show Author Affiliations
Michael Georgiopoulos, Univ. of Central Florida (United States)
J. Huang, Intel Corp. (United States)
Gregory L. Heileman, Univ. of New Mexico (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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