Model Complexity of Neural Networks in High-Dimensional Approximation

0360537 - ÚI 2013 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Kůrková, Věra
Model Complexity of Neural Networks in High-Dimensional Approximation.
Recent Advances in Intelligent Engineering Systems. Vol. 1. Berlin: Springer, 2012 - (Fodor, S.; Klempous, J.; Suárez Araujo, C.), s. 151-160. Studies in Computational Intelligence, 378. ISBN 978-3-642-23228-2. ISSN 1860-949X.
[INES 2010. International Conference on Intelligent Engineering Systems /14./. Las Palmas de Gran Canaria (ES), 05.05.2010-07.05.2010]
Grant CEP: GA MŠMT OC10047; GA MŠMT MEB040901
Výzkumný záměr: CEZ:AV0Z10300504
Klíčová slova: model complexity of neural networks * Gaussian radial-basis networks * dependence on input dimension
Kód oboru RIV: IN - Informatika

The role of dimensionality in approximation by neural networks is investigated. Methods from nonlinear approximation theory are used to describe sets of functions which can be approximated by neural networks with a polynomial dependence of model complexity on the input dimension. The results are illustrated by examples of Gaussian radial networks.
Trvalý link: http://hdl.handle.net/11104/0198055