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The Complexity of Learning SUBSEQ (A)

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Algorithmic Learning Theory (ALT 2006)

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

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Abstract

Higman showed that if A is any language then SUBSEQ(A) is regular, where SUBSEQ(A) is the language of all subsequences of strings in A. We consider the following inductive inference problem: given A(ε), A(0), A(1), A(00), ... learn, in the limit, a DFA for SUBSEQ(A). We consider this model of learning and the variants of it that are usually studied in inductive inference: anomalies, mindchanges, and teams.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science and Engineering, University of South Carolina, SC, 29208, Columbia

    Stephen Fenner

  2. Dept. of Computer Science and UMIACS, University of Maryland at College Park, College Park, MD, 20742, USA

    William Gasarch

Authors
  1. Stephen Fenner
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  2. William Gasarch
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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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Fenner, S., Gasarch, W. (2006). The Complexity of Learning SUBSEQ (A). 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_12

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  • DOI: https://doi.org/10.1007/11894841_12

  • 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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