| Exact learning of read-k disjoint DNF and not-so-disjoint DNF |
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Annual Workshop on Computational Learning Theory
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Proceedings of the fifth annual workshop on Computational learning theory
table of contents
Pittsburgh, Pennsylvania, United States
Pages: 71 - 76
Year of Publication: 1992
ISBN:0-89791-497-X
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Authors
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Howard Aizenstein
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Department of Computer Science and College of Medicine, University of Illinois, Urbana, Illinois
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Leonard Pitt
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Department of Computer Science, University of Illinois, Urbana, Illinois
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| Bibliometrics |
Downloads (6 Weeks): 13, Downloads (12 Months): 23, Citation Count: 7
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ABSTRACT
A polynomial-time algorithm is presented for exactly learning the class of read-k disjoint DNF formulas—boolean formulas in disjunctive normal form where each variable appears at most k) and every assignment to the variables satisfies at most one term of F. The (standard) protocol used allows the learning algorithm to query whether a given assignment of boolean variables satisfies the DNF formula to be learned (membership queries), as well as to obtain counterexamples to the correctness of its current hypothesis which can be any arbitrary DNF formula (equivalence queries). The formula output by the learning algorithm is logically equivalent to the formula to be learned. We show that this result also applies for a generalization of read-k disjoint DNF which we call read-k sat-j DNF; these are DNF formulas in which every variable appears at most k times and every assignment satisfies at most j terms.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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AHK89
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AHP92
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H. Aizenstein, L. Hellerstein, and L. Pitt. Read-thice dnf is hard to learn with membership and equivalence queries. Extended abstract, 1992.
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Han91
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KLPV87
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M. Kearns , M. Li , L. Pitt , L. Valiant, On the learnability of Boolean formulae, Proceedings of the nineteenth annual ACM conference on Theory of computing, p.285-295, January 1987, New York, New York, United States
[doi> 10.1145/28395.28426]
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CITED BY 7
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Nader H. Bshouty , Sally A. Goldman , Thomas R. Hancock , Sleiman Matar, Asking questions to minimize errors, Proceedings of the sixth annual conference on Computational learning theory, p.41-50, July 26-28, 1993, Santa Cruz, California, United States
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Avrim Blum , Roni Khardon , Eyal Kushilevitz , Leonard Pitt , Dan Roth, On learning Read-k-Satisfy-j DNF, Proceedings of the seventh annual conference on Computational learning theory, p.110-117, July 12-15, 1994, New Brunswick, New Jersey, United States
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Avrim Blum , Merrick Furst , Jeffrey Jackson , Michael Kearns , Yishay Mansour , Steven Rudich, Weakly learning DNF and characterizing statistical query learning using Fourier analysis, Proceedings of the twenty-sixth annual ACM symposium on Theory of computing, p.253-262, May 23-25, 1994, Montreal, Quebec, Canada
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