A class of asymptotically stable algorithms for learning-rate adaptation.
Algorithmica, 22(1-2) pp. 198–210.
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.
||convex optimization; neural learning; learning-rate adaptation; line search; stability criterion
||Knowledge Media Institute
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||08 Oct 2008 13:13
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