Second-Order Recurrent Neural Networks Can Learn Regular Grammars from Noisy Strings - researchr publication related

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Rafael C. Carrasco, Mikel L. Forcada. Second-Order Recurrent Neural Networks Can Learn Regular Grammars from Noisy Strings. In José Mira, Francisco Sandoval Hernández, editors, From Natural to Artificial Neural Computation, International Workshop on Artificial Neural Networks, IWANN 95, Malaga-Torremolinos, Spain, June 7-9, 1995, Proceedings. Volume 930 of Lecture Notes in Computer Science, pages 605-610, Springer, 1995.

The following publications are possibly variants of this publication:

Recurrent neural networks can learn simple, approximate regular languagesMikel L. Forcada, Antonio M. Corbí-Bellot, Marco Gori, Marco Maggini. ijcnn 1999: 1527-1532 [doi]

Inferring stochastic regular grammars with recurrent neural networksRafael C. Carrasco, Mikel L. Forcada, Laureano Santamaría. icgi 1996: 274-281

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