Model Complexity of Neural Networks and Integral Transforms

Authors:
Věra Kůrková
Affiliations:
Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Year:
2009

Citing 11
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Abstract

Model complexity of neural networks is investigated using tools from nonlinear approximation and integration theory. Estimates of network complexity are obtained from inspection of upper bounds on decrease of approximation errors in approximation of multivariable functions by networks with increasing numbers of units. The upper bounds are derived using integral transforms with kernels corresponding to various types of computational units. The results are applied to perceptron networks.