Title of article
Learning exponential state-growth languages by hill climbing
Author/Authors
W.، Tabor, نويسنده ,
Issue Information
روزنامه با شماره پیاپی سال 2003
Pages
-443
From page
444
To page
0
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.
Keywords
Learning capability , neural-network modularity , Storage capacity , two-hidden-layer feedforward networks (TLFNs)
Journal title
IEEE TRANSACTIONS ON NEURAL NETWORKS
Serial Year
2003
Journal title
IEEE TRANSACTIONS ON NEURAL NETWORKS
Record number
62825
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