A Simple Perceptron that Learns Non-Monotonic Rules

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We investigate the generalization ability of a simple perceptron trained in the off-line and on-line supervised modes. Examples are extracted from the teacher who is a non-monotonic perceptron. For this system, difficulties of training can be controlled continuously by changing a parameter of the teacher. We train the student by several learning strategies in order to obtain the theoretical lower bounds of generalization errors under various conditions. Asymptotic behavior of the learning curve has been derived, which enables us to determine the most suitable learning algorithm for a given value of the parameter controlling difficulties of training.
LaTeX 10 pages including 6 ps figures, using llncs.sty, Proc. of Theoretical Aspects of Neural Computation 97, to be published from Springer-Verlag

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Disordered Systems and Neural Networks

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http://salesiana.dossiersoluciones.com/handle/123456789/151891

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cond-mat/9708096
 http://arxiv.org/abs/cond-mat/9708096

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arXiv