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On learning and co-learning of minimal programs

  • Sanjay Jain
  • Efim Kinber
  • Rolf Wiehagen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1160)

Keywords

Input Function Recursive Function Inductive Inference Total Function Minimal Program 
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. 1.
    L. Blum and M. Blum. Toward a mathematical theory of inductive inference. Information and Control, 28:125–155, 1975.CrossRefGoogle Scholar
  2. 2.
    M. Blum. A machine-independent theory of the complexity of recursive functions. Journal of the ACM, 14:322–336, 1967.CrossRefGoogle Scholar
  3. 3.
    J. Case and C. Smith. Comparison of identification criteria for machine inductive inference. Theoretical Computer Science, 25:193–220, 1983.CrossRefGoogle Scholar
  4. 4.
    R. Freivalds. Minimal Gödel numbers and their identification in the limit. In Proceedings of the International Conference on Mathematical Foundations of Computer Science, Marianske Lazne, pages 219–225. Springer-Verlag, 1975. Lecture Notes in Computer Science 32.Google Scholar
  5. 5.
    R. Freivalds. Inductive inference of minimal programs. In M. Fulk and J. Case, editors, Proceedings of the Third Annual Workshop on Computational Learning Theory, pages 3–20. Morgan Kaufmann Publishers, Inc., August 1990.Google Scholar
  6. 6.
    R. Freivalds. Inductive inference of recursive functions: Qualitative theory. In J. Barzdins and D. Bjorner, editors, Baltic Computer Science. Lecture Notes in Computer Science 502, pages 77–110. Springer-Verlag, 1991.Google Scholar
  7. 7.
    R. Freivalds and S. Jain. Kolmogorov numberings and minimal identification. In Paul Vitanyi, editor, Computational Learning Theory, Second European Conference, EuroCOLT'95, Barcelona, Spain, pages 182–195. Springer-Verlag, March 1995. Lecture Notes in Artificial Intelligence 904.Google Scholar
  8. 8.
    R. Freivalds, M. Karpinski, and C. H. Smith. Co-learning of total recursive functions. In Proceedings of the Seventh Annual Conference on Computational Learning Theory, New Brunswick, New Jersey, pages 190–197. ACM Press, July 1994.Google Scholar
  9. 9.
    R. Freivalds and T. Zeugmann. Co-learning of recursive languages from positive data. Technical Report RIFIS-TR-CS-110, Kyushu University, 1995.Google Scholar
  10. 10.
    E. M. Gold. Language identification in the limit. Information and Control, 10:447–474, 1967.CrossRefGoogle Scholar
  11. 11.
    S. Jain. An infinite class of functions identifiable using minimal programs in all Kolmogorov numberings. International Journal of Foundations of Computer Science, 6(1):89–94, March 1995.CrossRefGoogle Scholar
  12. 12.
    R. Klette and R. Wiehagen. Research in the theory of inductive inference by GDR mathematicians — A survey. Information Sciences, 22:149–169, 1980.Google Scholar
  13. 13.
    S. Lange, R. Wiehagen, and T. Zeugmann. Learning by erasing. Technical Report RIFIS-TR-CS-122, Kyushu University, 1996.Google Scholar
  14. 14.
    D. Osherson, M. Stob, and S. Weinstein. Systems that Learn, An Introduction to Learning Theory for Cognitive and Computer Scientists. MIT Press, Cambridge, Mass., 1986.Google Scholar
  15. 15.
    H. Rogers. Theory of Recursive Functions and Effective Computability. McGraw-Hill, New York, 1967. Reprinted, MIT Press 1987.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Sanjay Jain
    • 1
  • Efim Kinber
    • 2
  • Rolf Wiehagen
    • 3
  1. 1.Dept. of ISCSNational University of SingaporeSingapore
  2. 2.Department of Computer ScienceSacred Heart UniversityFairfield
  3. 3.Fachbereich InformatikUniversität KaiserslauternKaiserslauternGermany

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