Estimation of Performance of the Hopfield Randomized Memory

Estimation of Performance of the Hopfield Randomized Memory We study the recognition capability of a Hopfield network, i.e., a neural network where interaction of nodes is determined by the Hebb transform. We present a formally rigorous derivation of an upper bound on the error probability for recognition of randomized objects. It is based on the standard technique of estimating large deviations by the Chebyshev–Chernov method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Problems of Information Transmission Springer Journals

Estimation of Performance of the Hopfield Randomized Memory

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Publisher
Kluwer Academic Publishers-Plenum Publishers
Copyright
Copyright © 2001 by MAIK “Nauka/Interperiodica”
Subject
Engineering; Communications Engineering, Networks; Electrical Engineering; Information Storage and Retrieval; Systems Theory, Control
ISSN
0032-9460
eISSN
1608-3253
D.O.I.
10.1023/A:1010474125796
Publisher site
See Article on Publisher Site

Abstract

We study the recognition capability of a Hopfield network, i.e., a neural network where interaction of nodes is determined by the Hebb transform. We present a formally rigorous derivation of an upper bound on the error probability for recognition of randomized objects. It is based on the standard technique of estimating large deviations by the Chebyshev–Chernov method.

Journal

Problems of Information TransmissionSpringer Journals

Published: Oct 7, 2004

References

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