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Proceedings Paper

Noisy desired signal in transient detection using neural networks
Author(s): Jose C. Principe; Abir Zahalka
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Paper Abstract

In this paper we show the effect that several desired signals have on the performance of a neural network dynamic classifier for transient detection. We compare performances of the same neural network trained with the conventional 1/0 desired signal, a prediction framework and a desired signal composed of noise during the background. This last choice is the one that works best. We show that in terms of statistical decision theory this choice of desired signal should work as well as the optimal a posteriori detector. We provide an explanation why the noise during the background works for transient detection. Finally we comment on the implications of this choice of desired signal for learning in biological networks.

Paper Details

Date Published: 5 November 1993
PDF: 9 pages
Proc. SPIE 2036, Chaos in Biology and Medicine, (5 November 1993); doi: 10.1117/12.162722
Show Author Affiliations
Jose C. Principe, Univ. of Florida (United States)
Abir Zahalka, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 2036:
Chaos in Biology and Medicine
William L. Ditto, Editor(s)

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