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Algorithmic learning from incomplete information: Principles and problems

  • Chapter 4 Artificial Intelligence
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  • First Online:
Machines, Languages, and Complexity (IMYCS 1988)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 381))

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References

  1. Dana Angluin and Carl Smith, A survey of inductive inference: theory and methods, Computing Surveys 15 (1983), 237–269

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  2. E. Mark Gold, Language identification in the limit, Information and Control 14 (1967), 447–474

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  3. Klaus P. Jantke and Hans-Rainer Beick, Combining postulates of naturalness in inductive inference, EIK 17 (1981) 8/9, 465–484

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  4. Reinhard Klette and Rolf Wiehagen, Research in the theory of inductive inference by GDR mathematicians — a survey, Inf. Sciences 22 (1980), 149–169

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Authors and Affiliations

  1. Dept. of Mathematics & Informatics, Leipzig University of Technology, P.O.Box 66, 7030, Leipzig, DDR

    Klaus P. Jantke

Authors
  1. Klaus P. Jantke
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J. Dassow J. Kelemen

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© 1989 Springer-Verlag Berlin Heidelberg

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Jantke, K.P. (1989). Algorithmic learning from incomplete information: Principles and problems. In: Dassow, J., Kelemen, J. (eds) Machines, Languages, and Complexity. IMYCS 1988. Lecture Notes in Computer Science, vol 381. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0015939

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  • DOI: https://doi.org/10.1007/BFb0015939

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-51516-6

  • Online ISBN: 978-3-540-48203-1

  • eBook Packages: Springer Book Archive

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