Learning strongly deterministic even linear languages from positive examples

Takeshi Koshiba, Erkki Mäkinen, Yuji Takada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

We consider the problem of learning deterministic even lin­ear languages from positive examples. By a “deterministic” even linear language we mean a language generated by an LR(k) even linear gram­mar. We introduce a natural subclass of LR(k) even linear languages, called LR(k) in the strong sense, and show that this subclass is learn- able in the limit from positive examples. Furthermore, we propose a learning algorithm that identifies this subclass in the limit with almost linear time in updating conjectures. As a corollary, in terms of even Un­ear grammars, we have a learning algorithm for k-reversible languages that is more efficient than the one proposed by Angluin[Ang82].

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 6th International Workshop, ALT 1995, Proceedings
PublisherSpringer Verlag
Pages41-54
Number of pages14
Volume997
ISBN (Print)3540604545, 9783540604549
Publication statusPublished - 1995
Externally publishedYes
Event6th International Workshop on Algorithmic Learning Theory, ALT 1995 - Fukuoka, Japan
Duration: 1995 Oct 181995 Oct 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume997
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Workshop on Algorithmic Learning Theory, ALT 1995
CountryJapan
CityFukuoka
Period95/10/1895/10/20

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Koshiba, T., Mäkinen, E., & Takada, Y. (1995). Learning strongly deterministic even linear languages from positive examples. In Algorithmic Learning Theory - 6th International Workshop, ALT 1995, Proceedings (Vol. 997, pp. 41-54). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 997). Springer Verlag.