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Dynamics of learning and generalization in perceptrons with constraints

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  • Horner, Heinz

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.

Suggested Citation

  • Horner, Heinz, 1993. "Dynamics of learning and generalization in perceptrons with constraints," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 200(1), pages 552-562.
    • Handle: RePEc:eee:phsmap:v:200:y:1993:i:1:p:552-562
    DOI: 10.1016/0378-4371(93)90560-Q
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    References listed on IDEAS

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    1. Parisi, Giorgio & Pietronero, Luciano, 1991. "Theory of stretched exponential relaxation and critical behavior at depinning for charge density waves," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 179(1), pages 16-38.
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