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European Conference on Machine Learning

ECML 2006: Machine Learning: ECML 2006 pp 90–101Cite as

Languages as Hyperplanes: Grammatical Inference with String Kernels

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 4212)

Abstract

Using string kernels, languages can be represented as hyperplanes in a high dimensional feature space. We present a new family of grammatical inference algorithms based on this idea. We demonstrate that some mildly context sensitive languages can be represented in this way and it is possible to efficiently learn these using kernel PCA. We present some experiments demonstrating the effectiveness of this approach on some standard examples of context sensitive languages using small synthetic data sets.

Keywords

  • Feature Space
  • Regular Language
  • Positive Data
  • Negative Data
  • Context Sensitive

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of London, Royal Holloway, Egham, TW20 0EX

    Alexander Clark, Christophe Costa Florêncio & Chris Watkins

Authors
  1. Alexander Clark
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  2. Christophe Costa Florêncio
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  3. Chris Watkins
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Editor information

Editors and Affiliations

  1. Knowledge Engineering Group, Technische Universität Darmstadt,  

    Johannes Fürnkranz

  2. Max Planck Institute for Computer Science, Saarbrücken, Germany

    Tobias Scheffer

  3. Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, Germany

    Myra Spiliopoulou

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© 2006 Springer-Verlag Berlin Heidelberg

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Clark, A., Florêncio, C.C., Watkins, C. (2006). Languages as Hyperplanes: Grammatical Inference with String Kernels. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds) Machine Learning: ECML 2006. ECML 2006. Lecture Notes in Computer Science(), vol 4212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11871842_13

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  • DOI: https://doi.org/10.1007/11871842_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45375-8

  • Online ISBN: 978-3-540-46056-5

  • eBook Packages: Computer ScienceComputer Science (R0)