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Proceedings Paper

Flexible resource-allocating network for noisy data
Author(s): Arindam Nag; Joydeep Ghosh
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Paper Abstract

The resource allocating network (RAN) provides a simple and powerful method for on-line modeling with incremental growth in model complexity. However, the network growing algorithm is susceptible to outliers in the output domain. Pruning techniques subsequently proposed for RAN, though satisfactory for dealing with outliers in the input domain, are incapable of removing units grown in response to outliers in the output domain. The addition of a coarse scale unit in response to an output outlier results in a much larger network where units are wasted to negate the effect of the spurious unit. The resulting network generalizes poorly. In this paper, we discuss the problems associated with RAN in the presence of outliers, and provide a modified learning algorithm which recognizes and prunes units associated with spurious data. We also present a strategy to modify the remaining units, once a unit is pruned.

Paper Details

Date Published: 25 March 1998
PDF: 9 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304846
Show Author Affiliations
Arindam Nag, Univ. of Texas/Austin (United States)
Joydeep Ghosh, Univ. of Texas/Austin (United States)

Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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