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A note on learning DNF formulas using equivalence and incomplete membership queries

  • Zhixiang Chen
Selected Papers Algorithmic Learning Theory
Part of the Lecture Notes in Computer Science book series (LNCS, volume 872)

Abstract

In this note, we prove with derandomization techniques that a subclass of DNF formulas with nonconstant number of terms is polynomial time learnable using equivalence and incomplete membership queries. Although many concept classes are known to be polynomial time learnable using equivalence and membership queries, so far only two concept classes are known to be polynomial time learnable (see, [AS] and [GM]) when incomplete membership queries are used.

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References

  1. [AS]
    D. Angluin, D. Slonim, “Learning monotone DNF formula with an incomplete membership oracle”, Proc of the 4th Annual Workshop on Computational Learning Theory, pages 139–146. Morgan Kaufmann Publishers, Inc., San Mateo, CA, 1991.Google Scholar
  2. [BR]
    A. Blum, S. Rudich, “Fast learning of k-term DNF formulas with queries”, Proc of the 24th Annual ACM Symposium on Theory of Computing, May 1992, pages 382–389.Google Scholar
  3. [BCJ]
    A. Blum, P. Chalasani, J. Jackson, “On learning embedded symmetric concepts” Proc of the Sixth Annual ACM Conference on Computational Learning Theory, pages 337–346.Google Scholar
  4. [GM]
    S. Goldman, H. Mathias, “Learning k-term DNF formulas with an incomplete membership oracle”, Proc of the 5th Annual Workshop on Computational Learning Theory, pages 72–77, Morgan Kaufmann Publishers, Inc., San Mateo, CA, 1992.Google Scholar
  5. [NN]
    J. Naor, M. Naor, “Small-bias probability space: efficient constructions and applications”, Proc of the 22th Annual ACM Symposium on Theory of Computing, May 1992, pages 213–223.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Zhixiang Chen
    • 1
  1. 1.Department of Computer ScienceBoston UniversityBoston

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