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Learnability of Automatic Classes

  • Sanjay Jain
  • Qinglong Luo
  • Frank Stephan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6031)

Abstract

The present work initiates the study of the learnability of automatic indexable classes which are classes of regular languages of a certain form. Angluin’s tell-tale condition characterizes when these classes are explanatorily learnable. Therefore, the more interesting question is when learnability holds for learners with complexity bounds, formulated in the automata-theoretic setting. The learners in question work iteratively, in some cases with an additional long-term memory, where the update function of the learner mapping old hypothesis, old memory and current datum to new hypothesis and new memory is automatic. Furthermore, the dependence of the learnability on the indexing is also investigated. This work brings together the fields of inductive inference and automatic structures.

Keywords

Inductive Inference Regular Language Iterative Learner Positive Data Hypothesis Space 
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

  • Sanjay Jain
    • 1
  • Qinglong Luo
    • 1
  • Frank Stephan
    • 2
  1. 1.Department of Computer ScienceNational University of SingaporeSingaporeRepublic of Singapore
  2. 2.Department of Mathematics and Department of Computer ScienceNational University of SingaporeSingaporeRepublic of Singapore

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