Learning monotone log-term DNF formulas

Yoshifumi Sakai, Akira Maruoka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings of the 7th Annual Conference on Computational Learning Theory, COLT 1994
PublisherAssociation for Computing Machinery
Pages165-172
Number of pages8
ISBN (Electronic)0897916557
DOIs
Publication statusPublished - 1994 Jul 16
Event7th Annual Conference on Computational Learning Theory, COLT 1994 - New Brunswick, United States
Duration: 1994 Jul 121994 Jul 15

Publication series

NameProceedings of the Annual ACM Conference on Computational Learning Theory
VolumePart F129415

Other

Other7th Annual Conference on Computational Learning Theory, COLT 1994
CountryUnited States
CityNew Brunswick
Period94/7/1294/7/15

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