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Learning by Switching Type of Information

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Book cover Algorithmic Learning Theory (ALT 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2225))

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Abstract

The present work is dedicated to the study of modes of datapresentation between text and informant within the framework of inductive inference. The model is such that the learner requests sequences of positive andnegativ e data andthe relations between the various formalizations in dependence on the number of switches between positive and negative data is investigated. In particular it is shown that there is a proper hierarchy of the notions of learning from standard text, in the basic switching model, in the newtext switching model and in the restart switching model. The last one of these turns out to be equivalent to the standard notion of learning from informant.

Supported in part by NUS grant number RP3992710.

Supported by the Deutsche Forschungsgemeinschaft (DFG) under the Heisenberg grant Ste 967/1-1

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Author information

Authors and Affiliations

  1. School of Computing, National University of Singapore, 119260, Singapore

    Sanjay Jain

  2. Mathematisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 294, 69120, Heidelberg, Germany

    Frank Stephan

Authors
  1. Sanjay Jain
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  2. Frank Stephan
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Editor information

Editors and Affiliations

  1. IBM, Thomas J.Watson Research Center, Room 33-237, P.O.Box 218, 10598, Yorktown, NY, USA

    Naoki Abe

  2. Dept.of Electrical Engineering and Computer Science, Tufts University, 161 College Ave., 02155, Medford, MA, USA

    Roni Khardon

  3. Medizinische Universität zu Lübeck,Inst.für Theoretische Informatik, Wallstr.40, 23560, Lübeck, Germany

    Thomas Zeugmann

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© 2001 Springer-Verlag Berlin Heidelberg

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Jain, S., Stephan, F. (2001). Learning by Switching Type of Information. In: Abe, N., Khardon, R., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2001. Lecture Notes in Computer Science(), vol 2225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45583-3_17

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  • DOI: https://doi.org/10.1007/3-540-45583-3_17

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42875-6

  • Online ISBN: 978-3-540-45583-7

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