A class of asymptotically stable algorithms for learning-rate adaptation

Rüger, Stefan (1998). A class of asymptotically stable algorithms for learning-rate adaptation. Algorithmica, 22(1-2) pp. 198–210.

DOI: https://doi.org/10.1007/PL00013830

Abstract

A stability criterion for learning is given. In the case of learning-rate adaptation of backpropagation, a class of asymptotically stable algorithms is presented and studied, including a convergence proof. Simulations demonstrate relevance and limitations.

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