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Bayesian Active Learning Using Arbitrary Binary Valued Queries

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Book cover Algorithmic Learning Theory (ALT 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6331))

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Abstract

We explore a general Bayesian active learning setting, in which the learner can ask arbitrary yes/no questions. We derive upper and lower bounds on the expected number of queries required to achieve a specified expected risk.

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Author information

Authors and Affiliations

  1. Machine Learning Department, Carnegie Mellon University,  

    Liu Yang

  2. Department of Statistics, Carnegie Mellon University,  

    Steve Hanneke

  3. Language Technologies Institute, Carnegie Mellon University,  

    Jaime Carbonell

Authors
  1. Liu Yang
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  2. Steve Hanneke
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  3. Jaime Carbonell
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Editor information

Editors and Affiliations

  1. Research School of Information Sciences and Engineering, Australian National University and NICTA, 0200, Canberra, ACT, Australia

    Marcus Hutter

  2. Department of Mathematics, National University of Singapore, Block S17, 10 Lower Kent Ridge Road, 119076, Singapore, Republic of Singapore

    Frank Stephan

  3. Department of Computer Science, University of London, Royal Holloway, TW20 0EX, Egham, Surrey, UK

    Vladimir Vovk

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

    Thomas Zeugmann

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© 2010 Springer-Verlag Berlin Heidelberg

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Yang, L., Hanneke, S., Carbonell, J. (2010). Bayesian Active Learning Using Arbitrary Binary Valued Queries. In: Hutter, M., Stephan, F., Vovk, V., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2010. Lecture Notes in Computer Science(), vol 6331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16108-7_8

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  • DOI: https://doi.org/10.1007/978-3-642-16108-7_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16107-0

  • Online ISBN: 978-3-642-16108-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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