Learning conjunctions of two unate DNF formulas: Computational and informational results

Aaron Feigelson, Lisa Hellerstein

    Research output: Contribution to conferencePaper

    Abstract

    We consider the class R2, consisting of conjunctions of two unate DNF formulas. This class is a generalization of the class of 2-clause CNF formulas, and of the class of unate DNF formulas, both of which are properly learnable in polynomial time with membership and equivalence queries. We show that R2 can be properly learned with a polynomial number of polynomial-size membership and equivalence queries, but that it cannot be learned in polynomial time unless P = NP. Thus the barrier to learning R2 is computational rather than informational. In proving our results, we use recent techniques developed for the membership and equivalence query model, as well as Bshouty's work on the monotone dimension. We pose some related open questions on learning DNF formulas of small monotone dimension.

    Original languageEnglish (US)
    Pages255-265
    Number of pages11
    StatePublished - 1996
    EventProceedings of the 1996 9th Annual Conference on Computational Learning Theory - Desenzano del Garda, Italy
    Duration: Jun 28 1996Jul 1 1996

    Other

    OtherProceedings of the 1996 9th Annual Conference on Computational Learning Theory
    CityDesenzano del Garda, Italy
    Period6/28/967/1/96

    ASJC Scopus subject areas

    • Computational Mathematics

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    Feigelson, A., & Hellerstein, L. (1996). Learning conjunctions of two unate DNF formulas: Computational and informational results. 255-265. Paper presented at Proceedings of the 1996 9th Annual Conference on Computational Learning Theory, Desenzano del Garda, Italy, .