Learning in the multilayer perceptron
- 7 June 1988
- journal article
- Published by IOP Publishing in Journal of Physics A: General Physics
- Vol. 21 (11) , 2643-2650
- https://doi.org/10.1088/0305-4470/21/11/021
Abstract
Learning in a generalised perceptron neutral network model is investigated by numerical simulation. It is found that the distribution of learning times is very broad and spreads out as the system size is increased. The mean number of steps, k, to learn a first-order task is found to increase with system size according to a power law (k) approximately Nalpha , alpha =1.86+or-0.05.Keywords
This publication has 3 references indexed in Scilit:
- Connectionistic models of boolean category representationBiological Cybernetics, 1986
- A theory of the learnableCommunications of the ACM, 1984
- Neural networks and physical systems with emergent collective computational abilities.Proceedings of the National Academy of Sciences, 1982