On-line learning of non-monotonic rules by simple perceptron

Abstract

We study the generalization ability of a simple perceptron which learns unlearnable rules. The rules are presented by a teacher perceptron with a non-monotonic transfer function. The student is trained in the on-line mode. The asymptotic behaviour of the generalization error is estimated under various conditions. Several learning strategies are proposed and improved to obtain the theoretical lower bound of the generalization error.

LaTeX 20 pages using IOP LaTeX preprint style file, 14 figures

Cited by in corpus (4)

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