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TR00-002 | 23rd December 1999
Michael Schmitt

#### Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks

We calculate lower bounds on the size of sigmoidal neural networks
that approximate continuous functions. In particular, we show that
for the approximation of polynomials the network size has to grow
as \$\Omega((\log k)^{1/4})\$ where \$k\$ is the degree of the polynomials.
This bound is ... more >>>

ISSN 1433-8092 | Imprint