A Simple Perceptron that Learns Non-Monotonic Rules
Abstract
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.
Cite
@article{arxiv.cond-mat/9708096,
title = {A Simple Perceptron that Learns Non-Monotonic Rules},
author = {Jun-ichi Inoue and Hidetoshi Nishimori and Yoshiyuki Kabashima},
journal= {arXiv preprint arXiv:cond-mat/9708096},
year = {2008}
}
Comments
LaTeX 10 pages including 6 ps figures, using llncs.sty, Proc. of Theoretical Aspects of Neural Computation 97, to be published from Springer-Verlag