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On learning context-free and context-sensitive languages
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
University of Queensland, Australia.
2002 (English)In: IEEE Transactions on Neural Networks, ISSN 1045-9227, E-ISSN 1941-0093, Vol. 13, no 2, 491-493 p.Article in journal (Refereed) Published
Abstract [en]

The long short-term memory (LSTM) is not the only neural network which learns a context sensitive language. Second-order sequential cascaded networks (SCNs) are able to induce means from a finite fragment of a context-sensitive language for processing strings outside the training set. The dynamical behavior of the SCN is qualitatively distinct from that observed in LSTM networks. Differences in performance and dynamics are discussed.

Place, publisher, year, edition, pages
New York: IEEE , 2002. Vol. 13, no 2, 491-493 p.
Keyword [en]
language, prediction, recurrent neural network (RNN)
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-3358DOI: 10.1109/72.991436ISI: 000174519400023Scopus ID: 2-s2.0-0036506051OAI: oai:DiVA.org:hh-3358DiVA: diva2:282049
Note
©2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Available from: 2009-12-18 Created: 2009-12-01 Last updated: 2010-12-09Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf