Title

Parallelizing The Fuzzy Artmap Algorithm On A Beowulf Cluster

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 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.

Publication Date

12-1-2005

Publication Title

Proceedings of the International Joint Conference on Neural Networks

Volume

1

Number of Pages

475-480

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/IJCNN.2005.1555877

Socpus ID

33745960398 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33745960398

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