Learning strongly deterministic even linear languages from positive examples

Takeshi Koshiba; Erkki Mäkinen; Yuji Takada

Learning strongly deterministic even linear languages from positive examples

Takeshi Koshiba, Erkki Mäkinen, Yuji Takada

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 language	English
Title of host publication	Algorithmic Learning Theory - 6th International Workshop, ALT 1995, Proceedings
Publisher	Springer Verlag
Pages	41-54
Number of pages	14
Volume	997
ISBN (Print)	3540604545, 9783540604549
Publication status	Published - 1995
Externally published	Yes
Event	6th International Workshop on Algorithmic Learning Theory, ALT 1995 - Fukuoka, Japan Duration: 1995 Oct 18 → 1995 Oct 20

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	997
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	6th International Workshop on Algorithmic Learning Theory, ALT 1995
Country/Territory	Japan
City	Fukuoka
Period	95/10/18 → 95/10/20

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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.

Learning strongly deterministic even linear languages from positive examples. / Koshiba, Takeshi; Mäkinen, Erkki; Takada, Yuji.

Algorithmic Learning Theory - 6th International Workshop, ALT 1995, Proceedings. Vol. 997 Springer Verlag, 1995. p. 41-54 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 997).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 997, Springer Verlag, pp. 41-54, 6th International Workshop on Algorithmic Learning Theory, ALT 1995, Fukuoka, Japan, 95/10/18.

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

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

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Learning strongly deterministic even linear languages from positive examples

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