Advertisement

On the duality between mechanistic learners and what it is they learn

  • Rūsiņš Freivalds
  • Carl H. Smith
Selected Papers Inductive Inference
Part of the Lecture Notes in Computer Science book series (LNCS, volume 744)

Abstract

All previous work in inductive inference and theoretical machine learning has taken the perspective of looking for a learning algorithm that successfully learns a collection of functions. In this work, we consider the perspective of starting with a set of functions, and considering the collection of learning algorithms that are successful at learning the given functions. Some strong dualities are revealed.

Keywords

Recursive Function Single Function Inductive Inference Identification Type Strong Duality 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Proceedings of the 1988 Workshop on Computational Learning Theory, Palo Alto, CA., 1988. Morgan Kaufmann Publishers.Google Scholar
  2. [2]
    Proceedings of the Second Annual Workshop on Computational Learning Theory, Palo Alto, CA., 1989. Morgan Kaufmann Publishers.Google Scholar
  3. [3]
    Proceedings of the Third Annual Workshop on Computational Learning Theory, Palo Alto, CA., 1990. Morgan Kaufmann Publishers.Google Scholar
  4. [4]
    Proceedings of the 1991 Workshop on Computational Learning Theory, Palo Alto, CA., 1991. Morgan Kaufmann Publishers.Google Scholar
  5. [5]
    Proceedings of the 1992 workshop on computational learning theory, 1992.Google Scholar
  6. [6]
    D. Angluin, W. I. Gasarch, and C. H. Smith. Training sequences. Theoretical Computer Science, 66:255–272, 1989.Google Scholar
  7. [7]
    D. Angluin and C. H. Smith. Inductive inference: Theory and methods. Computing Surveys, 15:237–269, 1983.CrossRefGoogle Scholar
  8. [8]
    D. Angluin and C. H. Smith. Inductive inference. In S. Shapiro, editor, Encyclopedia of Artificial Intelligence, pages 409–418. John Wiley and Sons Inc., 1987.Google Scholar
  9. [9]
    L. Blum and M. Blum. Toward a mathematical theory of inductive inference. Information and Control, 28:125–155, 1975.CrossRefGoogle Scholar
  10. [10]
    J. Case and C. Smith. Anomaly hierarchies of mechanized inductive inference. In Proceedings of the 10th Symposium on the Theory of Computing, pages 314–319, San Diego, CA, 1978.Google Scholar
  11. [11]
    J. Case and C. Smith. Comparison of identification criteria for machine inductive inference. Theoretical Computer Science, 25(2): 193–220, 1983.CrossRefGoogle Scholar
  12. [12]
    R. V. Freivalds and R. Wiehagen. Inductive inference with additional information. Elektronische Informationsverabeitung und Kybernetik, 15(4): 179–184, 1979.Google Scholar
  13. [13]
    E. M. Gold. Language identification in the limit. Information and Control, 10:447–474, 1967.CrossRefGoogle Scholar
  14. [14]
    M. Machtey and P. Young. An Introduction to the General Theory of Algorithms. North-Holland, New York, 1978.Google Scholar
  15. [15]
    L. Pitt. A characterization of probabilistic inference. Journal of the ACM, 36(2):383–433, 1989.CrossRefGoogle Scholar
  16. [16]
    C. H. Smith. The power of pluralism for automatic program synthesis. Journal of the ACM, 29(4):1144–1165, 1982.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Rūsiņš Freivalds
    • 1
  • Carl H. Smith
    • 2
  1. 1.Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLatvia
  2. 2.Department of Computer ScienceUniversity of MarylandCollege ParkUSA

Personalised recommendations