Globally optimal parameters for on-line learning in multilayer neural networks

Saad, David and Rattray, Magnus (1997). Globally optimal parameters for on-line learning in multilayer neural networks. Physical Review Letters, 79 (13), pp. 2578-2581.

Abstract

We present a framework for calculating globally optimal parameters, within a given time frame, for on-line learning in multilayer neural networks. We demonstrate the capability of this method by computing optimal learning rates in typical learning scenarios. A similar treatment allows one to determine the relevance of related training algorithms based on modifications to the basic gradient descent rule as well as to compare different training methods.

Publication DOI: https://doi.org/10.1103/PhysRevLett.79.2578
Divisions: Engineering & Applied Sciences > Mathematics
Engineering & Applied Sciences > Non-linearity and complexity research group
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Additional Information: Copyright of the American Physical Society
Uncontrolled Keywords: on-line learning,multilayer neural networks,learning rates,training algorithms
Published Date: 1997-09-29

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