Estimates of approximation rates by Gaussian radial-basis functions

被引：0

作者：

Kainen, Paul C. ^{[1
]}

Kurkova, Vera ^{[2
]}

Sanguineti, Marcello ^{[3
]}

机构：

[1] Georgetown Univ, Dept Math, Washington, DC 20057 USA

[2] Inst Comp Sci, Acad Sci Czech Republic, Prague, Czech Republic

[3] Inst Comp Sci, Acad Sci Czech Republic, Prague, Czech Republic

来源：

ADAPTIVE AND NATURAL COMPUTING ALGORITHMS, PT 2 | 2007年 / 4432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：

Rates of approximation by networks with Gaussian RBFs with varying widths are investigated. For certain smooth functions, upper bounds are derived in terms of a Sobolev-equivalent norm. Coefficients involved are exponentially decreasing in the dimension. The estimates are proven using Bessel potentials as auxiliary approximating functions.

引用

页码：11 / +

页数：3

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