A Simple Perceptron that Learns Non-Monotonic Rules

Details A Simple Perceptron that Learns Non-Monotonic Rules A Simple Perceptron that Learns Non-Monotonic Rules

1997-08-14

arxiv.org/abs/cond-mat/9708096v1

Physics

Condensed Matter

Disordered Systems and Neural Networks

LaTeX 10 pages including 6 ps figures, using llncs.sty, Proc. of Theoretical Aspects of Neural Computation 97, to be published

Scientific paper

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.

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