An algorithm to learn read-once threshold formulas, and transformations between learning models

Nader H. Bshouty, Thomas R. Hancock, Lisa Hellerstein, Marek Karpinski

    Research output: Contribution to journalArticle


    We present a membership query (i.e. black box interpolation) algorithm for exactly identifying the class of read-once formulas over the basis of Boolean threshold functions. We also present a catalogue of generic transformations that can be used to convert an algorithm in one learning model into an algorithm in a different model.

    Original languageEnglish (US)
    Pages (from-to)37-61
    Number of pages25
    JournalComputational Complexity
    Issue number1
    Publication statusPublished - Mar 1994



    • 68Q20
    • 68T05
    • equivalence queries
    • justifying assignments
    • learning algorithms
    • learning models
    • membership queries
    • read-once threshold formulas
    • Subject classifications
    • transformations

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computational Mathematics
    • Mathematics(all)
    • Computational Theory and Mathematics

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