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Learning by Constraint Relaxation

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Neural Networks and Soft Computing

Part of the book series: Advances in Soft Computing ((AINSC,volume 19))

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Summary

We present a soft computing approach to text processing and propose a constraint theoretic approach for machine learning called learning by constraint relaxation (LCR). Words do not convey exact meanings but act as soft constraints over possible meanings. LCR is based on this principle: a failure to recognize is an opportunity to learn. A rule-making language, RML, has been implemented to facilitate problem representation and experimentation with constraint relaxation problems. LCR has been used to extract knowledge from texts containing medical descriptions and informatics codes.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science, Florida Institute of Technology, Melbourne, Fl, 32901, USA

    John Day

Authors
  1. John Day
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Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Technical University Częstochowa, Al. Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

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© 2003 Springer-Verlag Berlin Heidelberg

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Day, J. (2003). Learning by Constraint Relaxation. In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_91

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  • DOI: https://doi.org/10.1007/978-3-7908-1902-1_91

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-0005-0

  • Online ISBN: 978-3-7908-1902-1

  • eBook Packages: Springer Book Archive

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