### Abstract

We consider the problem of learning deterministic even linear languages from positive examples. We show that, for any nonnegative integer k, the class of LR(k) even linear languages is not learnable from positive examples while there is a subclass called LRS(k), which is a natural subclass of LR(k) in the strong sense, learnable from positive examples. Our learning algorithm identifies this subclass in the limit with almost linear time in updating conjectures. As a corollary, in terms of even linear grammars, we have a learning algorithm for k-reversible languages that is more efficient than the one proposed by Angluin.

Original language | English |
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Pages (from-to) | 63-79 |

Number of pages | 17 |

Journal | Theoretical Computer Science |

Volume | 185 |

Issue number | 1 |

DOIs | |

Publication status | Published - 1997 Oct 10 |

Externally published | Yes |

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### ASJC Scopus subject areas

- Theoretical Computer Science
- Computer Science(all)

### Cite this

*Theoretical Computer Science*,

*185*(1), 63-79. https://doi.org/10.1016/S0304-3975(97)00016-9

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

Research output: Contribution to journal › Article

*Theoretical Computer Science*, vol. 185, no. 1, pp. 63-79. https://doi.org/10.1016/S0304-3975(97)00016-9

}

TY - JOUR

T1 - Learning deterministic even linear languages from positive examples

AU - Koshiba, Takeshi

AU - Mäkinen, Erkki

AU - Takada, Yuji

PY - 1997/10/10

Y1 - 1997/10/10

N2 - We consider the problem of learning deterministic even linear languages from positive examples. We show that, for any nonnegative integer k, the class of LR(k) even linear languages is not learnable from positive examples while there is a subclass called LRS(k), which is a natural subclass of LR(k) in the strong sense, learnable from positive examples. Our learning algorithm identifies this subclass in the limit with almost linear time in updating conjectures. As a corollary, in terms of even linear grammars, we have a learning algorithm for k-reversible languages that is more efficient than the one proposed by Angluin.

AB - We consider the problem of learning deterministic even linear languages from positive examples. We show that, for any nonnegative integer k, the class of LR(k) even linear languages is not learnable from positive examples while there is a subclass called LRS(k), which is a natural subclass of LR(k) in the strong sense, learnable from positive examples. Our learning algorithm identifies this subclass in the limit with almost linear time in updating conjectures. As a corollary, in terms of even linear grammars, we have a learning algorithm for k-reversible languages that is more efficient than the one proposed by Angluin.

UR - http://www.scopus.com/inward/record.url?scp=0031251289&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031251289&partnerID=8YFLogxK

U2 - 10.1016/S0304-3975(97)00016-9

DO - 10.1016/S0304-3975(97)00016-9

M3 - Article

AN - SCOPUS:0031251289

VL - 185

SP - 63

EP - 79

JO - Theoretical Computer Science

JF - Theoretical Computer Science

SN - 0304-3975

IS - 1

ER -