Study of the Behavior of a New Boosting Algorithm for Recurrent Neural Networks

Assaad, Mohammad; Boné, Romuald; Cardot, Hubert

doi:10.1007/11550907_28

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Boosting Recurrent Neural Network1

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Cited by 9 publications

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“…the residual error). Before presenting briefly our algorithm, studied in (Assaad et al, 2005), let us mention that in (Cook & Robinson, 1996) a boosting method is applied to the classification of phonemes, with RNNs as learners. The authors are the first ones to have noticed the implications of the internal memory of the RNNs on the boosting algorithm.…”

Section: Boosting Recurrent Neural Networkmentioning

confidence: 99%

Recurrent Neural Networks for Temporal Data Processing

Cardot¹

2011

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The RNNs (Recurrent Neural Networks) are a general case of arti cial neural networks where the connections are not feed-forward ones only. In RNNs, connections between units form directed cycles, providing an implicit internal memory. Those RNNs are adapted to problems dealing with signals evolving through time. Their internal memory gives them the ability to naturally take time into account. Valuable approximation results have been obtained for dynamical systems.During the last few years, several interesting neural networks developments have emerged such as spike nets and deep networks. This book will show that a lot of improvement and results are also present in the active  eld of RNNs.

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Section: Boosting Recurrent Neural Networkmentioning

confidence: 99%