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Inductive Inference of Languages from Samplings

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Algorithmic Learning Theory (ALT 2010)

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

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Abstract

We introduce, discuss, and study a model for inductive inference from samplings, formalizing an idea of learning different “projections” of languages. One set of our results addresses the problem of finding a uniform learner for all samplings of a language from a certain set when learners for particular samplings are available. Another set of results deals with extending learnability from a large natural set of samplings to larger sets. A number of open problems is formulated.

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Author information

Authors and Affiliations

  1. School of Computing, National University of Singapore, Singapore, 117417, Republic of Singapore

    Sanjay Jain

  2. Department of Computer Science, Sacred Heart University, Fairfield, CT, 06825-1000, U.S.A.

    Efim Kinber

Authors
  1. Sanjay Jain
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  2. Efim Kinber
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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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Jain, S., Kinber, E. (2010). Inductive Inference of Languages from Samplings. 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_27

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  • DOI: https://doi.org/10.1007/978-3-642-16108-7_27

  • 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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