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Learning by Constraint Relaxation

  • John Day
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 19)

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.

Keywords

Constraint Satisfaction Constraint Satisfaction Problem Soft Constraint Text Extraction Constraint Relaxation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • John Day
    • 1
  1. 1.Department of Computer ScienceFlorida Institute of TechnologyMelbourneUSA

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