his.sePublications
Change search
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
Rule Extraction from Recurrent Neural Networks: A Taxonomy and Review
University of Skövde, School of Humanities and Informatics.
2005 (English)In: Neural Computation, ISSN 0899-7667, E-ISSN 1530-888X, Vol. 17, no 6, 1223-1263 p.Article in journal (Refereed) Published
Abstract [en]

Rule extraction (RE) from recurrent neural networks (RNNs) refers to finding models of the underlying RNN, typically in the form of finite state machines, that mimic the network to a satisfactory degree while having the advantage of being more transparent. RE from RNNs can be argued to allow a deeper and more profound form of analysis of RNNs than other, more or less ad hoc methods. RE may give us understanding of RNNs in the intermediate levels between quite abstract theoretical knowledge of RNNs as a class of computing devices and quantitative performance evaluations of RNN instantiations. The development of techniques for extraction of rules from RNNs has been an active field since the early 1990s. This article reviews the progress of this development and analyzes it in detail. In order to structure the survey and evaluate the techniques, a taxonomy specifically designed for this purpose has been developed. Moreover, important open research issues are identified that, if addressed properly, possibly can give the field a significant push forward.

Place, publisher, year, edition, pages
MIT Press, 2005. Vol. 17, no 6, 1223-1263 p.
National Category
Engineering and Technology
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-1646DOI: 10.1162/0899766053630350ISI: 000228694000001Scopus ID: 2-s2.0-18444364992OAI: oai:DiVA.org:his-1646DiVA: diva2:31922
Available from: 2008-01-09 Created: 2008-01-09 Last updated: 2013-04-09Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Jacobsson, Henrik
By organisation
School of Humanities and Informatics
In the same journal
Neural Computation
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 104 hits
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