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

被引：5

作者：

Inoue, J ^{[1
]}

Nishimori, H ^{[1
]}

Kabashima, Y ^{[1
]}

机构：

[1] TOKYO INST TECHNOL,INTERDISCIPLINARY GRAD SCH ENGN SCI,DEPT COMPUTAT INTELLIGENCE & SYST SCI,YOKOHAMA,KANAGAWA 226,JAPAN

来源：

JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL | 1997年 / 30卷 / 11期

关键词：

D O I：

10.1088/0305-4470/30/11/012

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

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.

引用

页码：3795 / 3816

页数：22

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