Jose Castro, Jimmy Secretan, Michael Georgiopoulos, Ronald DeMara, Georgios Anagnostopoulos, Avelino Gonzalez  Home/Search
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Abstract: Fuzzy ARTMAP neural networks have been proven to be good classifiers on a variety of classification problems. However, the time that it takes Fuzzy ARTMAP to converge to a solution increases rapidly as the number of patterns used for training increases. In this paper we examine the time that it takes Fuzzy ARTMAP to converge to a solution and we propose a coarse grain parallelization technique, based on a pipeline approach, to speed-up the training process. In particular, we have... (Update)
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BibTeX entry: (Update)
@misc{ castro-pipelining,
author = "Jose Castro and Jimmy Secretan and Michael Georgiopoulos and Ronald DeMara
and Georgios Anagnostopoulos and Avelino Gonzalez",
title = "Pipelining of Fuzzy ARTMAP without Matchtracking: Correctness, Performance
Bound, and Beowulf Evaluation",
url = "citeseer.ist.psu.edu/740905.html" }
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