### Abstract

Based on the uniform distribution PAC learning model, the learnability for monotone disjunctive normal form formulas with at most O(logn) terms (O(log n)-term MDNF) is investigated. Using the technique of restriction, an algorithm that learns O(logn)-term MDNF in polynomial time is given.

Original language | English |
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Title of host publication | Proceedings of the 7th Annual Conference on Computational Learning Theory, COLT 1994 |

Publisher | Association for Computing Machinery |

Pages | 165-172 |

Number of pages | 8 |

ISBN (Electronic) | 0897916557 |

DOIs | |

Publication status | Published - 1994 Jul 16 |

Event | 7th Annual Conference on Computational Learning Theory, COLT 1994 - New Brunswick, United States Duration: 1994 Jul 12 → 1994 Jul 15 |

### Publication series

Name | Proceedings of the Annual ACM Conference on Computational Learning Theory |
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Volume | Part F129415 |

### Other

Other | 7th Annual Conference on Computational Learning Theory, COLT 1994 |
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Country | United States |

City | New Brunswick |

Period | 94/7/12 → 94/7/15 |

### ASJC Scopus subject areas

- Software
- Theoretical Computer Science
- Artificial Intelligence

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## Cite this

Sakai, Y., & Maruoka, A. (1994). Learning monotone log-term DNF formulas. In

*Proceedings of the 7th Annual Conference on Computational Learning Theory, COLT 1994*(pp. 165-172). (Proceedings of the Annual ACM Conference on Computational Learning Theory; Vol. Part F129415). Association for Computing Machinery. https://doi.org/10.1145/180139.181095