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Languages as Hyperplanes: Grammatical Inference with String Kernels

  • Conference paper

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


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.


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

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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.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45375-8

  • Online ISBN: 978-3-540-46056-5

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