DocumentCode
1166480
Title
Learning exponential state-growth languages by hill climbing
Author
Tabor, Whitney
Author_Institution
Dept. of Psychol., Connecticut Univ., Storrs, CT, USA
Volume
14
Issue
2
fYear
2003
fDate
3/1/2003 12:00:00 AM
Firstpage
444
Lastpage
446
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
fractals; learning (artificial intelligence); probability; recurrent neural nets; exponential state-growth languages; fractal learning; hill climbing; infinite state languages; probability distribution; recurrent neural networks; recursive computations; Computer architecture; Computer networks; Counting circuits; Fractals; Learning automata; Neural networks; Probability distribution; Recurrent neural networks; Turing machines; Vocabulary;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/TNN.2003.809421
Filename
1189642
Link To Document