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Inductive inference of functions from noised observations

  • Jan Grabowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 265)

Abstract

This paper treats the inductive inference of computable functions the observations of which are falsified by noise. The effect of noise is assumed to satisfy a recursion theoretic randomness condition. It turns out that under three natural assumptions (finite range, reliable identifiability of the function class and "proper" noise function) the identifiability is preserved up to a finite set of anomalies.

Keywords

Proper Subset Finite Subset Recursive Function Computable Function Inductive Inference 
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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References

  1. /KW/.
    Klette, R. / Wiehagen, R., Research in the theory of inductive inference by GDR mathematicians — a survey. Information Sciences 22(1980) 149–169Google Scholar
  2. /Gr/.
    Grabowski,J. Ein Fortsetzungsprinzip in der Erkennungstheorie und seine Anwendung. In: Strukturerkennung diskreter kybernetischer Systems (ed. R.Lindner, H.Thiele). Seminarberichte der Sektion Mathematik Nr. 82. Humboldt-Universität zu Berlin 1986Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1987

Authors and Affiliations

  • Jan Grabowski
    • 1
  1. 1.Organisations- und Rechenzentrum Humboldt-Universität zu BerlinBerlinDDR

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