Parallelizing The Fuzzy Artmap Algorithm On A Beowulf Cluster
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 propose a coarse grain parallelization technique, based on a pipeline approach, to speed-up Fuzzy ARTMAP's training process. In particular, we first parallelized Fuzzy ARTMAP, without the match-tracking mechanism, and then we parallelized Fuzzy ARTMAP with the match-tracking mechanism. Results run on a Beowulf cluster with a well known large database (Forrest Covertype database from the UCI repository) show linear speedup with respect to the number of processors used in the pipeline. © 2005 IEEE.
Proceedings of the International Joint Conference on Neural Networks
Number of Pages
Article; Proceedings Paper
Source API URL
Secretan, Jimmy; Castro, José; and Georgiopoulos, Michael, "Parallelizing The Fuzzy Artmap Algorithm On A Beowulf Cluster" (2005). Scopus Export 2000s. 3292.