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. © 2009 Springer Berlin Heidelberg.
Cite
CITATION STYLE
Kůrková, V. (2009). Model complexity of neural networks and integral transforms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5768 LNCS, pp. 708–717). https://doi.org/10.1007/978-3-642-04274-4_73
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