Title

Parallelization of Fuzzy ARTMAP to improve its convergence speed: The network partitioning approach and the data partitioning approach

Authors

Authors

J. Castro; M. Georgiopoulos; J. Secretan; R. F. DeMara; G. Anagnostopoulos;A. Gonzalez

Comments

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Abbreviated Journal Title

Nonlinear Anal.-Theory Methods Appl.

Keywords

Fuzzy ARTMAP; BEOWULF parallel processing; Data partitioning; Network; partitioning; Mathematics, Applied; Mathematics

Abstract

One of the properties of FAM, which can be both an asset and a liability, is its capacity to produce newneurons (templates) on demand to represent classification categories. This property allows FAM to automatically adapt to the database without having to arbitrarily specify network structure. We provide two methods for speeding up the FAM algorithm. The first one, referred to as the data partitioning approach, partitions the data into subsets for independent processing. The second one, referred to as the network partitioning approach, uses a pipeline to distribute the work between processes during training. We provide experimental results on a Beowulf cluster of workstations for both approaches that confirm the speedup of the modifications. (C) 2005 Elsevier Ltd. All rights reserved.

Journal Title

Nonlinear Analysis-Theory Methods & Applications

Volume

63

Issue/Number

5-7

Publication Date

1-1-2005

Document Type

Article

Language

English

First Page

E877

Last Page

E889

WOS Identifier

WOS:000208147800088

ISSN

0362-546X

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