Parallelization of Fuzzy ARTMAP to improve its convergence speed: The network partitioning approach and the data partitioning approach
Abbreviated Journal Title
Nonlinear Anal.-Theory Methods Appl.
Fuzzy ARTMAP; BEOWULF parallel processing; Data partitioning; Network; partitioning; Mathematics, Applied; Mathematics
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.
Nonlinear Analysis-Theory Methods & Applications
"Parallelization of Fuzzy ARTMAP to improve its convergence speed: The network partitioning approach and the data partitioning approach" (2005). Faculty Bibliography 2000s. 5045.