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    Learnability of solutions to conjunctive queries

    Chen, Hubie and Valeriote, M. (2019) Learnability of solutions to conjunctive queries. Journal of Machine Learning Research 20 (67), pp. 1-28. ISSN 1532-4435.

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    Abstract

    The problem of learning the solution space of an unknown formula has been studied inmultiple embodiments in computational learning theory. In this article, we study a familyof such learning problems; this family contains, for each relational structure, the problem oflearning the solution space of an unknown conjunctive query evaluated on the structure. Aprogression of results aimed to classify the learnability of each of the problems in this family,and thus far a culmination thereof was a positive learnability result generalizing all previousones. This article completes the classification program towards which this progression ofresults strived, by presenting a negative learnability result that complements the mentionedpositive learnability result. In addition, a further negative learnability result is exhibited,which indicates a dichotomy within the problems to which the first negative result applies.In order to obtain our negative results, we make use of universal-algebraic concepts.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): concept learning, computational learning theory, dichotomy theorems, re-ductions, universal algebra
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Hubie Chen
    Date Deposited: 10 Jun 2019 09:08
    Last Modified: 09 Aug 2023 12:46
    URI: https://eprints.bbk.ac.uk/id/eprint/27755

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