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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Speeding up backpropagation with Multiplicative Batch Update Step
verfasst von : Pedro Cruz
Erschienen in: Adaptive and Natural Computing Algorithms
Verlag: Springer Vienna
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Updating steps in a backpropagation neural network with multiplicative factors
u
> 1 and
d
< 1 has been presented by several authors. The istatistics field of Stochastic Approximation has a close relation with back-propagation algorithms. Recent theoretical results in this field show that for functions of one variable, different values of
u
and
d
can produce very different results: fast convergence at the cost of a poor solution, slow convergence with a better solution, or produce a fast move towards a solution but without converging. To speed up backpropagation in a simple manner we propose a batch step adaptation technique for the online backpropagation algorithm based on theoretical results on simple cases.