Learning efficiency of very simple grammars from positive data

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

3 Citations (Scopus)

Abstract

The class of very simple grammars is known to be polynomial-time identifiable in the limit from positive data. This paper gives even more general discussion on the efficiency of identification of very simple grammars from positive data, which includes both positive and negative results. In particular, we present an alternative efficient inconsistent learning algorithm for very simple grammars.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 18th International Conference, ALT 2007, Proceedings
PublisherSpringer Verlag
Pages227-241
Number of pages15
ISBN (Print)9783540752240
DOIs
Publication statusPublished - 2007 Jan 1
Externally publishedYes
Event18th International Conference on Algorithmic Learning Theory, ALT 2007 - Sendai, Japan
Duration: 2007 Oct 12007 Oct 4

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4754 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th International Conference on Algorithmic Learning Theory, ALT 2007
CountryJapan
CitySendai
Period07/10/107/10/4

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yoshinaka, R. (2007). Learning efficiency of very simple grammars from positive data. In Algorithmic Learning Theory - 18th International Conference, ALT 2007, Proceedings (pp. 227-241). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4754 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-75225-7_20