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Parameterized Learnability of k-Juntas and Related Problems

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Algorithmic Learning Theory (ALT 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4754))

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Abstract

We study the parameterized complexity of learning k-juntas and some variations of juntas. We show the hardness of learning k-juntas and subclasses of k-juntas in the PAC model by reductions from a W[2]-complete problem. On the other hand, as a consequence of a more general result we show that k-juntas are exactly learnable with improper equivalence queries and access to a W[P] oracle.

Work supported by a DST-DAAD project grant for exchange visits.

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Author information

Authors and Affiliations

  1. The Institute of Mathematical Sciences, Chennai 600 113, India

    Vikraman Arvind

  2. Institut für Informatik, Humboldt Universität zu Berlin, Germany

    Johannes Köbler

  3. Sidonia Systems, Grubmühl 20, D-82131 Stockdorf, Germany

    Wolfgang Lindner

Authors
  1. Vikraman Arvind
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  2. Johannes Köbler
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  3. Wolfgang Lindner
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Editor information

Editors and Affiliations

  1. RSISE @ ANU and SML @ NICTA, Canberra,, ACT, 0200, Australia

    Marcus Hutter

  2. Columbia University, NY, P.O. Box, New York, USA

    Rocco A. Servedio

  3. Graduate School of Information Sciences, Tohoku University,, Sendai 980-8579, Japan

    Eiji Takimoto

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© 2007 Springer-Verlag Berlin Heidelberg

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Arvind, V., Köbler, J., Lindner, W. (2007). Parameterized Learnability of k-Juntas and Related Problems. In: Hutter, M., Servedio, R.A., Takimoto, E. (eds) Algorithmic Learning Theory. ALT 2007. Lecture Notes in Computer Science(), vol 4754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75225-7_13

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  • DOI: https://doi.org/10.1007/978-3-540-75225-7_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75224-0

  • Online ISBN: 978-3-540-75225-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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