Arbitrary classification by a novel generating-shrinking algorithm

Yan Qiu Chen; David Thomas; Mark S. Nixon

doi:10.1117/12.172490

1 February 1994 Arbitrary classification by a novel generating-shrinking algorithm

Yan Qiu Chen, David Thomas, Mark S. Nixon

Proceedings Volume 2093, Substance Identification Analytics; (1994) https://doi.org/10.1117/12.172490
Event: Substance Identification Technologies, 1993, Innsbruck, Austria

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.

Citation Download Citation

Yan Qiu Chen, David Thomas, and Mark S. Nixon "Arbitrary classification by a novel generating-shrinking algorithm", Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); https://doi.org/10.1117/12.172490

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