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International Colloquium on Grammatical Inference

ICGI 2002: Grammatical Inference: Algorithms and Applications pp 296–298Cite as

Software for Analysing Recurrent Neural Nets That Learn to Predict Non-regular Languages

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2484))

Abstract

Training first-order recurrent neural networks to predict symbol sequences from context-free or context-sensitive languages is known as a hard task. A prototype software system has been implemented that can train these networks and evaluate performance after training. A special version of the (1+1)-ES algorithm is employed that allows both incremental and non-incremental training. The system provides advanced analysis tools that take not only the final solution but the whole sequence of intermediate solutions into account. For each of these solutions a qualitative analysis of hidden unit activity and a quantitative evaluation of generalisation ability can be performed.

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References

  1. Chalup, S. K. & Blair, A. D.: Incremental Training of First Order Recurrent Neural Networks to Predict a Context-Sensitive Language. Submitted

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Author information

Authors and Affiliations

  1. School of Electrical Engineering & Computer Science, The University of Newcastle, Callaghan, 2308, Australia

    Stephan K. Chalup

  2. School of Computer Science & Engineering, The University of New South Wales, Sydney, 2052, Australia

    Alan D. Blair

Authors
  1. Stephan K. Chalup
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  2. Alan D. Blair
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Editor information

Editors and Affiliations

  1. Perot Systems Nederland B.V., Hoefseweg 1, 3821 AE, Amersfoort, The Netherlands

    Pieter Adriaans (Senior Research Advisor, Professor of Learning and Adaptive Systems) (Senior Research Advisor, Professor of Learning and Adaptive Systems)

  2. ILLC/Computation and Complexity Theory, Universiteit van Amsterdam, Plantage Muidergracht 24, 1018 TV, Amsterdam, The Netherlands

    Pieter Adriaans (Senior Research Advisor, Professor of Learning and Adaptive Systems) (Senior Research Advisor, Professor of Learning and Adaptive Systems)

  3. School of Electrical Engineering and Computer Science, University of Newcastle, University Drive, Callaghan, NSW, 2308, Australia

    Henning Fernau

  4. Wilhelm-Schickard-Institut für Informatik, Universität Tübingen, Sand 13, 72076, Tübingen, Germany

    Henning Fernau

  5. FNWI/ILLC, Cognitive Systems and Information Processing Group, Universiteit van Amsterdam, Room B-5.39, Nieuwe Achtergracht 166, 1018 WV, Amsterdam, The Netherlands

    Menno van Zaanen

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© 2002 Springer-Verlag Berlin Heidelberg

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Chalup, S.K., Blair, A.D. (2002). Software for Analysing Recurrent Neural Nets That Learn to Predict Non-regular Languages. In: Adriaans, P., Fernau, H., van Zaanen, M. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2002. Lecture Notes in Computer Science(), vol 2484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45790-9_25

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  • DOI: https://doi.org/10.1007/3-540-45790-9_25

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44239-4

  • Online ISBN: 978-3-540-45790-9

  • eBook Packages: Springer Book Archive

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