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On Exact Learning Monotone DNF from Membership Queries

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Algorithmic Learning Theory (ALT 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8776))

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Abstract

In this paper, we study the problem of learning a monotone DNF with at most s terms of size (number of variables in each term) at most r (s term r-MDNF) from membership queries. This problem is equivalent to the problem of learning a general hypergraph using hyperedge-detecting queries, a problem motivated by applications arising in chemical reactions and genome sequencing.

We first present new lower bounds for this problem and then present deterministic and randomized adaptive algorithms with query complexities that are almost optimal. All the algorithms we present in this paper run in time linear in the query complexity and the number of variables n. In addition, all of the algorithms we present in this paper are asymptotically tight for fixed r and/or s.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Technion, Haifa, Israel

    Hasan Abasi & Nader H. Bshouty

  2. IBM Research, Haifa, Israel

    Hanna Mazzawi

Authors
  1. Hasan Abasi
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  2. Nader H. Bshouty
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  3. Hanna Mazzawi
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Editor information

Editors and Affiliations

  1. Montanuniversitaet Leoben, 8700, Leoben, Austria

    Peter Auer

  2. Department of Philosophy, King’s College, WC2R 2LS, London, UK

    Alexander Clark

  3. Division of Computer Science, Hokkaido University, N-14, W-9, 060-0814, Sapporo, Japan

    Thomas Zeugmann

  4. Department of Computer Science, University of Regina, S4S 0A2, Regina, SK, Canada

    Sandra Zilles

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© 2014 Springer International Publishing Switzerland

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Abasi, H., Bshouty, N.H., Mazzawi, H. (2014). On Exact Learning Monotone DNF from Membership Queries. In: Auer, P., Clark, A., Zeugmann, T., Zilles, S. (eds) Algorithmic Learning Theory. ALT 2014. Lecture Notes in Computer Science(), vol 8776. Springer, Cham. https://doi.org/10.1007/978-3-319-11662-4_9

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  • DOI: https://doi.org/10.1007/978-3-319-11662-4_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11661-7

  • Online ISBN: 978-3-319-11662-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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