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Combining Numeric and Symbolic Learning Techniques

  • Richard H. GrangerJr.
  • Jeffrey C. Schlimmer
Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 12)

Abstract

Incremental learning from examples in a noisy domain is a difficult problem in machine learning. In this paper we divide the task into two subproblems and present a combination of numeric and symbolic approaches that yields robust learning of boolean characterizations. We have implemented this method in a computer program and present its empirical learning performance in the presence of varying amounts of noise.

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Copyright information

© Kluwer Academic Publishers 1986

Authors and Affiliations

  • Richard H. GrangerJr.
    • 1
  • Jeffrey C. Schlimmer
    • 1
  1. 1.Irvine Computational Intelligence Project, Department of Information and Computer ScienceUniversity of CaliforniaIrvineUSA

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