Learning a Subclass of Regular Patterns in Polynomial Time

  • John Case
  • Sanjay Jain
  • Rüdiger Reischuk
  • Frank Stephan
  • Thomas Zeugmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2842)


Presented is an algorithm (for learning a subclass of erasing regular pattern languages) which can be made to run with arbitrarily high probability of success on extended regular languages generated by patterns π of the form x 0 α 1 x 1 ... α m x m for unknown m but known c, from number of examples polynomial in m (and exponential in c), where α 1...α m are variables and where α 1,...,α m are each strings of constants or terminals of length c. This assumes that the algorithm randomly draws samples with natural and plausible assumptions on the distribution.

The more general looking case of extended regular patterns which alternate between a variable and fixed length constant strings, beginning and ending with either a variable or a constant string is similarly handled.


Polynomial Time Regular Pattern Regular Language Inductive Logic Programming Target Pattern 
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 2003

Authors and Affiliations

  • John Case
    • 1
  • Sanjay Jain
    • 2
  • Rüdiger Reischuk
    • 3
  • Frank Stephan
    • 4
  • Thomas Zeugmann
    • 3
  1. 1.Dept. of Computer and Information SciencesUniversity of DelawareNewarkUSA
  2. 2.School of ComputingNational University of SingaporeSingapore
  3. 3.Institute for Theoretical InformaticsUniversity at LübeckLübeckGermany
  4. 4.Mathematisches InstitutUniversität HeidelbergHeidelbergGermany

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