Survey of learning results in adaptive resonance theory (ART) architectures

Michael Georgiopoulos; J. Huang; Gregory L. Heileman

doi:10.1117/12.205147

6 April 1995 Survey of learning results in adaptive resonance theory (ART) architectures

Michael Georgiopoulos, J. Huang, Gregory L. Heileman

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205147
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

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Michael Georgiopoulos, J. Huang, and Gregory L. Heileman "Survey of learning results in adaptive resonance theory (ART) architectures", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205147

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KEYWORDS

Neural networks

Binary data

Network architectures

Lithium

Analog electronics

Fourier transforms

Image classification

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