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Iterative Learning from Positive Data and Negative Counterexamples

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Book cover Algorithmic Learning Theory (ALT 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4264))

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Abstract

A model for learning in the limit is defined where a (so-called iterative) learner gets all positive examples from the target language, tests every new conjecture with a teacher (oracle) if it is a subset of the target language (and if it is not, then it receives a negative counterexample), and uses only limited long-term memory (incorporated in conjectures). Three variants of this model are compared: when a learner receives least negative counterexamples, the ones whose size is bounded by the maximum size of input seen so far, and arbitrary ones. We also compare our learnability model with other relevant models of learnability in the limit, study how our model works for indexed classes of recursive languages, and show that learners in our model can work in non-U-shaped way — never abandoning the first right conjecture.

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Author information

Authors and Affiliations

  1. School of Computing, National University of Singapore, 117543, Singapore

    Sanjay Jain

  2. Department of Computer Science, Sacred Heart University, Fairfield, CT, 06432-1000, 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. Departament de Llenguatges i Sistemes Informàtics Laboratori d’Algorísmica Relacional, Complexitat i Aprenentatge, Universitat Politècnica de Catalunya, Barcelona,  

    José L. Balcázar

  2. Google, 1600 Amphitheatre Parkway, 94043, Mountain View, CA, USA

    Philip M. Long

  3. Department of Computer Science and Department of Mathematics, National University of Singapore, 117543, Singapore, Republic of Singapore

    Frank Stephan

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© 2006 Springer-Verlag Berlin Heidelberg

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Jain, S., Kinber, E. (2006). Iterative Learning from Positive Data and Negative Counterexamples. In: Balcázar, J.L., Long, P.M., Stephan, F. (eds) Algorithmic Learning Theory. ALT 2006. Lecture Notes in Computer Science(), vol 4264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11894841_15

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  • DOI: https://doi.org/10.1007/11894841_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46649-9

  • Online ISBN: 978-3-540-46650-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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