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Proceedings Paper

Boosted ellipsoid ARTMAP
Author(s): Georgios C. Anagnostopoulos; Michael Georgiopoulos; Steven J. Verzi; Gregory L. Heileman
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Paper Abstract

Ellipsoid ARTMAP (EAM) is an adaptive-resonance-theory neural network architecture that is capable of successfully performing classification tasks using incremental learning. EAM achieves its task by summarizing labeled input data via hyper-ellipsoidal structures (categories). A major property of EAM, when using off-line fast learning, is that it perfectly learns its training set after training has completed. Depending on the classification problems at hand, this fact implies that off-line EAM training may potentially suffer from over-fitting. For such problems we present an enhancement to the basic Ellipsoid ARTMAP architecture, namely Boosted Ellipsoid ARTMAP (bEAM), that is designed to simultaneously improve the generalization properties and reduce the number of created categories for EAM's off-line fast learning. This is being accomplished by forcing EAM to be tolerant about occasional misclassification errors during fast learning. An additional advantage provided by bEAM's desing is the capability of learning inconsistent cases, that is, learning identical patterns with contradicting class labels. After we present the theory behind bEAM's enhancements, we provide some preliminary experimental results, which compare the new variant to the original EAM network, Probabilistic EAM and three different variants of the Restricted Coulomb Energy neural network on the square-in-a-square classification problem.

Paper Details

Date Published: 11 March 2002
PDF: 12 pages
Proc. SPIE 4739, Applications and Science of Computational Intelligence V, (11 March 2002); doi: 10.1117/12.458722
Show Author Affiliations
Georgios C. Anagnostopoulos, Univ. of Central Florida (United States)
Michael Georgiopoulos, Univ. of Central Florida (United States)
Steven J. Verzi, Univ. of New Mexico (United States)
Gregory L. Heileman, Univ. of New Mexico (United States)

Published in SPIE Proceedings Vol. 4739:
Applications and Science of Computational Intelligence V
Kevin L. Priddy; Paul E. Keller; Peter J. Angeline, Editor(s)

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