Tight bounds on rates of neural-network approximation

被引：0
|
作者：
Kurková, V
Sanguineti, M
机构：
[1] Acad Sci Czech Republ, Inst Comp Sci, Prague 18207 8, Czech Republic
[2] Univ Genoa, Dept Commun Comp & Syst Sci, I-16145 Genoa, Italy
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：
Complexity of neural networks measured by the number of hidden units is studied in terms of rates of approximation. Limitations of improvements of upper bounds of the order of O(n(-1/2)) on such rates are investigated for perceptron networks with some periodic and sigmoidal activation functions.
引用
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页码：277 / 282
页数：6
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