Learning k-term monotone Boolean formulae

  • Yoshifumi Sakai
  • Akira Maruoka
Technical Papers Approximate Learning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 743)


Valiant introduced a computational model of learning by examples, and gave a precise definition of learnability based on the model. Since then, much effort has been devoted to characterize learnable classes of concepts on this model. Among such learnable classes is the one, denoted k-term MDNF, consisting of monotone disjunctive normal form formulae with at most k terms. In literature, k-term MDNF is shown to be learnable under the assumption that examples are drawn according to the uniform distribution. In this paper we generalize the result to obtain the statement that k-term MDNF is learnable even if positive examples are drawn according to such distribution that the maximum of the ratio of the probabilities of two positive examples is bounded from above by some polynomial.


Boolean Function Target Function Learnable Classis Boolean Formula Disjunctive Normal Form 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Yoshifumi Sakai
    • 1
  • Akira Maruoka
    • 1
  1. 1.Faculty of EngineeringTohoku UniversitySendaiJapan

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