By Topic

Learning exponential state-growth languages by hill climbing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Tabor, W. ; Dept. of Psychol., Connecticut Univ., Storrs, CT, USA

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.

Published in:

Neural Networks, IEEE Transactions on  (Volume:14 ,  Issue: 2 )