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Learning from Positive Data and Negative Counterexamples: A Survey

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Book cover Computing with New Resources

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8808))

Abstract

The article presents state of the art on learning languages in the limit from full positive data and negative counterexamples to overextending conjectures. In the main model, the learner can store in its long-term memory all data seen so far. Variants of this model are considered where the learner always gets least counterexamples, or counterexamples bounded by the maximal size of positive data seen. All these variants are also considered for the model, where the learner does not have long-term memory, but can use the last conjecture. Capabilities, properties, and relationships between these models (and some other variations) are surveyed. Also, a variant of the main model restricted to learning classes definable by finite automata by learners definable by finite automata is considered.

Sanjay Jain—Supported in part by NUS grant number C-252-000-087-001.

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Author information

Authors and Affiliations

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

    Sanjay Jain

  2. Department of Computer Science, Sacred Heart University, Fairfield, CT, 06432-1000, USA

    Efim Kinber

Authors
  1. Sanjay Jain
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  2. Efim Kinber
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Correspondence to Efim Kinber .

Editor information

Editors and Affiliations

  1. Department of Computer Science, The University of Auckland, Auckland, New Zealand

    Cristian S. Calude

  2. University of Latvia, Riga, Latvia

    Rūsiņš Freivalds

  3. School of Informatics, Kyoto University, Kyoto, Japan

    Iwama Kazuo

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Jain, S., Kinber, E. (2014). Learning from Positive Data and Negative Counterexamples: A Survey. In: Calude, C., Freivalds, R., Kazuo, I. (eds) Computing with New Resources. Lecture Notes in Computer Science(), vol 8808. Springer, Cham. https://doi.org/10.1007/978-3-319-13350-8_24

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  • DOI: https://doi.org/10.1007/978-3-319-13350-8_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13349-2

  • Online ISBN: 978-3-319-13350-8

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