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    Learnability of relatively quantified generalized formulas

    Bulatov, A.A. and Chen, Hubie and Dalmau, V. (2004) Learnability of relatively quantified generalized formulas. In: David, S. and Case, J. and Maruoka, A. (eds.) 15th International Conference: Algorithmic Learning Theory. Lecture Notes in Computer Science 3244. Springer, pp. 365-379. ISBN 9783540233565.

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    Abstract

    In this paper we study the learning complexity of a vast class of quantifed formulas called Relatively Quantified Generalized Formulas. This class of formulas is parameterized by a set of predicates, called a basis. We give a complete classification theorem, showing that every basis gives rise to quantified formulas that are either polynomially learnable with equivalence queries, or not polynomially predictable with membership queries under some cryptographic assumption. We also provide a simple criteria distinguishing the learnable cases from the non-learnable cases.

    Metadata

    Item Type: Book Section
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 09 Mar 2021 15:59
    Last Modified: 09 Mar 2021 15:59
    URI: https://eprints.bbk.ac.uk/id/eprint/43346

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