Learning exponential state-growth languages by hill climbing

Authors:
W. Tabor
Affiliations:
Dept. of Psychol., Connecticut Univ., Storrs, CT, USA
Venue:
IEEE Transactions on Neural Networks
Year:
2003

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Abstract

Training recurrent neural networks on infinite state languages has been successful with languages in which the minimal number of machine states grows linearly with the length of the sentence, but has faired poorly with exponential state-growth languages. The new architecture learns several exponential state-growth languages in near perfect by hill climbing.