Generalization performances of perceptrons

被引：0

作者：

Gavin, G ^{[1
]}

机构：

[1] Univ Lyon 2, Lab ERIC, F-69676 Bron, France

来源：

ARTIFICIAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS | 2001年 / 2130卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents new results about the confidence bounds on the generalization performances of perceptrons. It deals with regression problems. It is shown that the probability to get a generalization error greater than the empirical error plus a precision E, depends on the number of inputs and on the magnitude of the coefficients of the combination. The result presented does not require to bound a priori the magnitude of these coefficients, the size and the number of layers.

引用

页码：259 / 264

页数：6

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