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On discovering functional relationships when observations are noisy: 2D Case

  • J. Ćwik
  • J. Koronacki
  • J. M. Żytkow
Communications 7B Learning and Knowledge Discovery
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1609)

Abstract

A heuristic method of model selection for a nonlinear regression problem on R 2 is proposed and discussed. The method is based on combining nonparametric statistical techniques for generalized additive models with an implementation of the Equation Finder of Zembowicz and Żytkow (1992). Given the inherent instability of such approaches to model selection when data are noisy, a special procedure for stabilization of the selection is an important target of the method proposed.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • J. Ćwik
    • 1
  • J. Koronacki
    • 1
    • 2
  • J. M. Żytkow
    • 1
    • 3
  1. 1.Institute of Computer SciencePolish Academy of SciencesPoland
  2. 2.Polish-Japanese Institute of Computer TechniquesPoland
  3. 3.Computer Science Department, UNC CharlottePoland

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