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String Extension Learning Using Lattices

  • Anna Kasprzik
  • Timo Kötzing
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6031)

Abstract

The class of regular languages is not identifiable from positive data in Gold’s language learning model. Many attempts have been made to define interesting classes that are learnable in this model, preferably with the associated learner having certain advantageous properties. Heinz ’09 presents a set of language classes called String Extension (Learning) Classes, and shows it to have several desirable properties.

In the present paper, we extend the notion of String Extension Classes by basing it on lattices and formally establish further useful properties resulting from this extension. Using lattices enables us to cover a larger range of language classes including the pattern languages, as well as to give various ways of characterizing String Extension Classes and its learners. We believe this paper to show that String Extension Classes are learnable in a very natural way, and thus worthy of further study.

Keywords

Minimum Element Computable Function Inductive Inference Regular Language Language Class 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Anna Kasprzik
    • 1
  • Timo Kötzing
    • 2
  1. 1.FB IV – Abteilung InformatikUniversität TrierTrierGermany
  2. 2.Department 1: Algorithms and ComplexityMax-Planck-Institut für InformatikSaarbrückenGermany

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