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Proceedings Paper

Fuzzy ART and Fuzzy ARTMAP with adaptively weighted distances
Author(s): Dimitrios Charalampidis; Georgios C. Anagnostopoulos; Michael Georgiopoulos; Takis Kasparis
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Paper Abstract

In this paper, we introduce a modification of the Fuzzy ARTMAP (FAM) neural network, namely, the Fuzzy ARTMAP with adaptively weighted distances (FAMawd) neural network. In FAMawd we substitute the regular L1-norm with a weighted L1-norm to measure the distances between categories and input patterns. The distance-related weights are a function of a category's shape and allow for bias in the direction of a category's expansion during learning. Moreover, the modification to the distance measurement is proposed in order to study the capability of FAMawd in achieving more compact knowledge representation than FAM, while simultaneously maintaining good classification performance. For a special parameter setting FAMawd simplifies to the original FAM, thus, making FAMawd a generalization of the FAM architecture. We also present an experimental comparison between FAMawd and FAM on two benchmark classification problems in terms of generalization performance and utilization of categories. Our obtained results illustrate FAMawd's potential to exhibit low memory utilization, while maintaining classification performance comparable to FAM.

Paper Details

Date Published: 11 March 2002
PDF: 12 pages
Proc. SPIE 4739, Applications and Science of Computational Intelligence V, (11 March 2002); doi: 10.1117/12.458723
Show Author Affiliations
Dimitrios Charalampidis, Univ. of New Orleans (United States)
Georgios C. Anagnostopoulos, Univ. of Central Florida (United States)
Michael Georgiopoulos, Univ. of Central Florida (United States)
Takis Kasparis, Univ. of Central Florida (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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