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Neural networks, error-correcting codes, and polynomials over the binary n-cube

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2 Author(s)
J. Bruck ; IBM Almaden Res. Center, San Jose, CA, USA ; M. Blaum

Several ways of relating the concept of error-correcting codes to the concept of neural networks are presented. Performing maximum-likelihood decoding in a linear block error-correcting code is shown to be equivalent to finding a global maximum of the energy function of a certain neural network. Given a linear block code, a neural network can be constructed in such a way that every codeword corresponds to a local maximum. The connection between maximization of polynomials over the n-cube and error-correcting codes is also investigated; the results suggest that decoding techniques can be a useful tool for solving such maximization problems. The results are generalized to both nonbinary and nonlinear codes

Published in:

IEEE Transactions on Information Theory  (Volume:35 ,  Issue: 5 )