Dynamics of learning and generalization in perceptrons with constraints

Heinz Horner

Physica A: Statistical Mechanics and its Applications, 1993, vol. 200, issue 1, 552-562

Abstract: Depending on the kind of constraints imposed on the weights of a perceptron, learning can be a combinatorially hard problem. As an example of this type, I discuss a perception with binary weights comparing results obtained from replica theory, dynamic mean field theory and simulated annealing. Contrary to the replica calculation, dynamics yields information about the performance of a polynomial algorithm in a situation where the best solution cannot be found in polynomial time. I also discuss improved learning algorithms and results for finite size perceptrons.

Date: 1993
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:200:y:1993:i:1:p:552-562

DOI: 10.1016/0378-4371(93)90560-Q

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