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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Mirikitani, D. Nikolaev, N. |
| Copyright Year | 2007 |
| Description | Author affiliation: Univ. of London, London (Mirikitani, D.; Nikolaev, N.) |
| Abstract | This paper develops a Bayesian approach to recursive second order training of recurrent neural networks. A general recursive Levenberg-Marquardt algorithm is elaborated using Bayesian regularization. Individual local regularization hyperparameters as well as an output noise hyper-parameter are reestimated in order to maximize the weight posterior distribution and to produce a well generalizing network model. The proposed algorithm performs a computationally stable sequential Hessian estimation with RTRL derivatives. Experimental investigations using benchmark and practical data sets show that the developed algorithm outperforms the standard RTRL and extended Kalman training algorithms for recurrent nets, as well as feed forward and finite impulse response neural filters, on time series modeling. |
| Starting Page | 282 |
| Ending Page | 287 |
| File Size | 461672 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424413799 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2007.4370969 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-08-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Bayesian methods Recurrent neural networks Kalman filters Finite impulse response filter Training data Filtering algorithms Neural networks Equations Standards development Feeds |
| Content Type | Text |
| Resource Type | Article |
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