Communications of the ACM
On the learnability of Boolean formulae
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Learning decision trees from random examples needed for learning
Information and Computation
Equivalence queries and approximate fingerprints
COLT '89 Proceedings of the second annual workshop on Computational learning theory
When won't membership queries help?
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
Learning decision trees using the Fourier spectrum
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
Learning 2u DNF formulas and ku decision trees
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Exact learning of read-twice DNF formulas (extended abstract)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Learning read-once formulas with queries
Journal of the ACM (JACM)
Linear time deterministic learning of k-term DNF
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Asking questions to minimize errors
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
On learning Read-k-Satisfy-j DNF
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Weakly learning DNF and characterizing statistical query learning using Fourier analysis
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Learning DNF over the uniform distribution using a quantum example oracle
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
A simple algorithm for learning O(log n)-term DNF
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Learning functions represented as multiplicity automata
Journal of the ACM (JACM)
Generalized Graph Colorability and Compressibility of Boolean Formulae
ISAAC '98 Proceedings of the 9th International Symposium on Algorithms and Computation
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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.