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Exact learning of read-k disjoint DNF and not-so-disjoint DNF
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Source Annual Workshop on Computational Learning Theory archive
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
Authors
Howard Aizenstein  Department of Computer Science and College of Medicine, University of Illinois, Urbana, Illinois
Leonard Pitt  Department of Computer Science, University of Illinois, Urbana, Illinois
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 0,   Downloads (12 Months): 10,   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.

AHK89
 
AHP92
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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CITED BY  7

Collaborative Colleagues:
Howard Aizenstein: colleagues
Leonard Pitt: colleagues