Biased learning in Boolean perceptrons

Osame Kinouchi and Nestor Caticha

Physica A: Statistical Mechanics and its Applications, 1992, vol. 185, issue 1, 411-416

Abstract: The generalization ability of Hebbian Boolean perceptrons can be improved by a kind of feedback mechanism in which the student net judges the difficulty of a new example from its previous experience. It is shown that by giving a higher weight to the hard examples both generalization and learning abilities can be increased. Analytical as well as numerical results are presented for both cases where the examples are drawn at random or selected in an intelligent form.

Date: 1992
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:185:y:1992:i:1:p:411-416

DOI: 10.1016/0378-4371(92)90482-6

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