Complexity estimates based on integral transforms induced by computational units

Authors: Věra Kůrková
Publication year: 2012
Publication type: Článek v odborném periodiku
Periodical: Neural Networks
Projects: Learning of functional relationships from high-dimensional data
Bibliographic citation: KŮRKOVÁ, Věra. Complexity estimates based on integral transforms induced by computational units. Neural Networks. 2012, 33(September), 160-167. ISSN 0893-6080. E-ISSN 1879-2782
Dostupné z: doi:10.1016/j.neunet.2012.05.002
DOI: 10.1016/j.neunet.2012.05.002
ID: 377111

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.