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Generalizing over Several Learning Settings

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Grammatical Inference: Theoretical Results and Applications (ICGI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6339))

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Abstract

We recapitulate inference from membership and equivalence queries, positive and negative samples. Regular languages cannot be learned from one of those information sources only [1,2,3]. Combinations of two sources allowing regular (polynomial) inference are MQs and EQs [4], MQs and positive data [5,6], positive and negative data [7,8]. We sketch a meta-algorithm fully presented in [9] that generalizes over as many combinations of those sources as possible. This includes a survey of pairings for which there are no well-studied algorithms.

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Authors and Affiliations

  1. University of Trier,  

    Anna Kasprzik

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  1. Anna Kasprzik
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Editors and Affiliations

  1. Departamento de Sistemas Informáticos y Computatión, Universidad Politécnica de Valencia, Camino de Vera s/n, 46022, Valencia, Spain

    José M. Sempere  & Pedro García  & 

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Kasprzik, A. (2010). Generalizing over Several Learning Settings. In: Sempere, J.M., García, P. (eds) Grammatical Inference: Theoretical Results and Applications. ICGI 2010. Lecture Notes in Computer Science(), vol 6339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15488-1_28

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  • DOI: https://doi.org/10.1007/978-3-642-15488-1_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15487-4

  • Online ISBN: 978-3-642-15488-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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