Learning orthogonal F-Horn formulas

Eiji Takimoto, Akira Miyashiro, Akira Maruoka, Yoshifumi Sakai

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In the PAC-learning, or the query learning model, it has been an important open problem to decide whether the class of DNF and CNF formulas is leamable. Recently, it was pointed out that the problem of PAC-learning for these classes with membership queries can be reduced to that of learning for the class of k-quasi Horn formulas with membership and equivalence queries. A k-quasi Horn formula is a CNF formula with each clause containing at most k unnegated literals. In this paper, notions of F-Horn formulas and l-F-Horn formulas, which are extensions of k-quasi formulas, are introduced, and it is shown that the problem of query learning for DNF and CNF formulas with membership and equivalence queries can be reduced to that for l-F-Horn formulas for an appropriate choice of F. It is shown that under a condition on F, the class of orthogonal F-Horn formulas is learnable with membership, equivalence and subset queries. Moreover, it is shown that under the same condition the class of orthogonal l-F-Horn formulas is learnable with membership and equivalence queries. For the latter result, the condition of orthogonality of F-Horn formulas is crucial because, if the statement held without the condition, then the result would imply that DNF and CNF are exactly learnable with membership and equivalence queries.

Original languageEnglish
Pages (from-to)177-190
Number of pages14
JournalTheoretical Computer Science
Issue number1
Publication statusPublished - Oct 10 1997
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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