### 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 |
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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) |
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Volume | 997 |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Other

Other | 6th International Workshop on Algorithmic Learning Theory, ALT 1995 |
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Country | Japan |

City | Fukuoka |

Period | 95/10/18 → 95/10/20 |

### Fingerprint

### ASJC Scopus subject areas

- Theoretical Computer Science
- Computer Science(all)

### Cite this

*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.

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

*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.

}

TY - GEN

T1 - Learning strongly deterministic even linear languages from positive examples

AU - Koshiba, Takeshi

AU - Mäkinen, Erkki

AU - Takada, Yuji

PY - 1995

Y1 - 1995

N2 - 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].

AB - 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].

UR - http://www.scopus.com/inward/record.url?scp=84947938312&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84947938312&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84947938312

SN - 3540604545

SN - 9783540604549

VL - 997

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 41

EP - 54

BT - Algorithmic Learning Theory - 6th International Workshop, ALT 1995, Proceedings

PB - Springer Verlag

ER -