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Proceedings Paper

Arbitrary classification by a novel generating-shrinking algorithm
Author(s): Yan Qiu Chen; David Thomas; Mark S. Nixon
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Paper Abstract

A novel algorithm is proposed in this paper, which builds and then shrinks a three-layer feed- forward neural network to achieve arbitrary classification in the n-dimensional Euclidean space. The algorithm offers guaranteed convergence and a 100% correct classification rate on training patterns, as well as an explicit generalization rule for predicting how a trained network generalizes to patterns that did not appear in training. Moreover, this generalization rule is continuously adjustable from an equal-angle measure to an equal-distance measure via a single reference number to allow adaptation of performance for different requirements.

Paper Details

Date Published: 1 February 1994
PDF: 10 pages
Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); doi: 10.1117/12.172490
Show Author Affiliations
Yan Qiu Chen, Univ. of Southampton (United Kingdom)
David Thomas, Univ. of Southampton (United Kingdom)
Mark S. Nixon, Univ. of Southampton (United Kingdom)

Published in SPIE Proceedings Vol. 2093:
Substance Identification Analytics
James L. Flanagan; Richard J. Mammone; Albert E. Brandenstein; Edward Roy Pike; Stelios C. A. Thomopoulos; Marie-Paule Boyer; H. K. Huang; Osman M. Ratib, Editor(s)

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