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Hypothesis Spaces for Learning

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Language and Automata Theory and Applications (LATA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5457))

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Abstract

In this paper we survey some results in inductive inference showing how learnability of a class of languages may depend on hypothesis space chosen. We also discuss results which consider how learnability is effected if one requires learning with respect to every suitable hypothesis space. Additionally, optimal hypothesis spaces, using which every learnable class is learnable, is considered.

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Author information

Authors and Affiliations

  1. Department of Computer Science, National University of Singapore, Singapore, 117590, Republic of Singapore

    Sanjay Jain

Authors
  1. Sanjay Jain
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Editor information

Editors and Affiliations

  1. Research Group on Mathematical Linguistics, Rovira i Virgili University, Av. Catalunya, 35, 43002, Tarragona, Spain

    Adrian Horia Dediu

  2. Research Group on Mathematical Linguistics, Universitat Rovira i Virgili, Tarragona, Spain

    Armand Mihai Ionescu

  3. European Research Council Executive Agency, 16 Place Rogier, 1210, Brussels, Belgium

    Carlos Martín-Vide

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Jain, S. (2009). Hypothesis Spaces for Learning. In: Dediu, A.H., Ionescu, A.M., Martín-Vide, C. (eds) Language and Automata Theory and Applications. LATA 2009. Lecture Notes in Computer Science, vol 5457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00982-2_4

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  • DOI: https://doi.org/10.1007/978-3-642-00982-2_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00981-5

  • Online ISBN: 978-3-642-00982-2

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