Abbreviations
by J. A. P Erez-ortiz, J. Calera-rubio, M. L. Forcada
http://www.dlsi.ua.es/~japerez/pub/ps/icann2001a-postera4.ps.gz
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Abstract:
This paper studies the use of recurrent neural networks for predicting the next symbol in a sequence. The focus is on online prediction, a task much harder than the classical offline grammatical inference with neural networks. Different kinds of sequence sources are considered: finitestate machines, chaotic sources, and texts in human language. Two algorithms are used for network training: real-time recurrent learning and the decoupled extended Kalman filter.
Citations
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| 10 | A learning algorithm for continually training recurrent neural networks – Williams, Zipser - 1989 |
| 6 | Arithmetic coding + statistical modeling = data compression – Nelson - 1991 |
| 1 | K oteles (1999), "Extracting finite-state representations from recurrent neural networks trained on chaotic symbolic sequences – no, P |
