The learning dynamics of a universal approximator

West, Ansgar H. L., Saad, David and Nabney, Ian T. The learning dynamics of a universal approximator. Advances in Neural Information Processing Systems, 9 , pp. 288-294.

Abstract

The learning properties of a universal approximator, a normalized committee machine with adjustable biases, are studied for on-line back-propagation learning. Within a statistical mechanics framework, numerical studies show that this model has features which do not exist in previously studied two-layer network models without adjustable biases, e.g., attractive suboptimal symmetric phases even for realizable cases and noiseless data.

Divisions: Engineering & Applied Sciences > Mathematics
Engineering & Applied Sciences > Non-linearity and complexity research group
Engineering & Applied Sciences > Computer science
Engineering & Applied Sciences > Computer science research group
Additional Information: Copyright of the Massachusetts Institute of Technology Press (MIT Press)
Uncontrolled Keywords: approximator,back-propagation,symmetric phases,realizable cases,noiseless data

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