Approximation of functions from Korobov spaces by deep convolutional neural networks

被引：0
|
作者：
Tong Mao
Ding-Xuan Zhou
机构：
[1] City University of Hong Kong,School of Data Science
[2] University of Sydney,School of Mathematics and Statistics
来源：
Advances in Computational Mathematics | 2022年 / 48卷
关键词：
Machine learning; Deep convolutional neural networks; Curse of dimensionality; Korobov spaces; 68T07; 41A25;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The efficiency of deep convolutional neural networks (DCNNs) has been demonstrated empirically in many practical applications. In this paper, we establish a theory for approximating functions from Korobov spaces by DCNNs. It verifies rigorously the efficiency of DCNNs in approximating functions of many variables with some variable structures and their abilities in overcoming the curse of dimensionality.
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