| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 405514 | Neural Networks | 2012 | 8 Pages |
Abstract
Integral transforms with kernels corresponding to computational units are exploited to derive estimates of network complexity. The estimates are obtained by combining tools from nonlinear approximation theory and functional analysis together with representations of functions in the form of infinite neural networks. The results are applied to perceptron networks.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Věra Kůrková,