Learning strongly deterministic even linear languages from positive examples

Takeshi Koshiba, Erkki Mäkinen, Yuji Takada

研究成果

4 被引用数 (Scopus)

抄録

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].

本文言語English
ホスト出版物のタイトルAlgorithmic Learning Theory - 6th International Workshop, ALT 1995, Proceedings
出版社Springer Verlag
ページ41-54
ページ数14
997
ISBN(印刷版)3540604545, 9783540604549
出版ステータスPublished - 1995
外部発表はい
イベント6th International Workshop on Algorithmic Learning Theory, ALT 1995 - Fukuoka, Japan
継続期間: 1995 10 181995 10 20

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
997
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

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

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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