Abstract
This article deals with back-propagation, a learning method for neural nets. It is shown, first, how the introduction of test cycles brings out a great improvement in the convergence rate and, second, that costly experiments used to adjust learning-relevant parameters could be dispensed with.
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References
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© 1991 Springer-Verlag Berlin Heidelberg
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Salomon, R. (1991). Improved convergence rate of back-propagation with dynamic adaption of the learning rate. In: Schwefel, HP., Männer, R. (eds) Parallel Problem Solving from Nature. PPSN 1990. Lecture Notes in Computer Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029763
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DOI: https://doi.org/10.1007/BFb0029763
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-54148-6
Online ISBN: 978-3-540-70652-6
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