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Proceedings Paper

Implementation of parallel self-organizing map for the classification of images
Author(s): Weigang Li; Nilton Correia da Silva
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Paper Abstract

A study of Parallel Self-Organizing Map (Parallel-SOM) is proposed to modify Self-Organizing Map for parallel computing environments. In this model, the conventional repeated learning procedure is modified to learn just once. The once learning manner is more similar to human learning and memorizing activities. During training, every connection between neurons of input/output layers is considered as an independent processor. In this way, all elements of every matrix are calculated simultaneously. This synchronization feature improves the weight updating sequence significantly. In this paper, the detail sequence of Parallel-SOM is demonstrated through the classification of coin for deeply understanding the properties of the proposed model. In conventional computing environment (one processor), Parallel- SOM can be implemented without the once learning and parallel weight updating features. As an application, its implementation for the classification of the meteorological radar images is also shown.

Paper Details

Date Published: 22 March 1999
PDF: 9 pages
Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); doi: 10.1117/12.342883
Show Author Affiliations
Weigang Li, Univ. of Brazil (Brazil)
Nilton Correia da Silva, Univ. of Brazil (Brazil)

Published in SPIE Proceedings Vol. 3722:
Applications and Science of Computational Intelligence II
Kevin L. Priddy; Paul E. Keller; David B. Fogel; James C. Bezdek, Editor(s)

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