New,Faster Algorithms for Supervised Competitive Learning: Counterpropagation and Adaptive-Resonance Functionality |
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Authors: | Korn Granino A |
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Affiliation: | (1) ECE Department, University of Arizona, Tucson, AZ 7750, South Lakeshore Road, #15, Chelan, WA |
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Abstract: | Hecht-Nielsen's counterpropagation networks often learn to associate input and output patterns more quickly than backpropagation networks. But simple competitive learning cannot separate closely spaced input patterns without adaptive-resonance-like (ART) functionality which prevents neighboring patterns from stealing each other's templates. We demonstrate pseudo-ART functionality with a new, simple, and very fast algorithm which requires no pattern normalization at all. Competition can be based on either Euclidean or L1-norm matching. In the latter case, the new algorithm emulates fuzzy ART. We apply the pseudo-ART scheme to several new types of counterpropagation networks, including one based on competition among combined input/output patterns, and discuss application with and without noise. |
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