Communications of the ACM
Computational limitations on learning from examples
Journal of the ACM (JACM)
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Small-bias probability spaces: efficient constructions and applications
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
When won't membership queries help?
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
Machine Learning
Machine Learning
Computational learning theory: survey and selected bibliography
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
A technique for upper bounding the spectral norm with applications to learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Learning k-term DNF formulas with an incomplete membership oracle
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
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 visual concepts and DNF formulae
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
On the limits of proper learnability of subclasses of DNF formulas
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Learning unions of boxes with membership and equivalence queries
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
On learning counting functions with queries
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Exploiting random walks for learning
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Learning from a consistently ignorant teacher
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
On the learnability of Zn-DNF formulas (extended abstract)
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Simple learning algorithms using divide and conquer
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 from examples with unspecified attribute values (extended abstract)
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Efficient dualization of O(log n)-term monotone disjunctive normal forms
Discrete Applied Mathematics
A Machine Learning Approach to Rapid Development of XML Mapping Queries
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
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This paper presents an algorithm that uses equivalence and membership queries to learn the class of k-term DNF formulas in time O(n•2o(k)), where n is the number of input variables. This improves upon previous O(nk) bounds and allows one to learn DNF of O(log n) terms in polynomial time. We present the algorithm in its most natural form as a randomized algorithm, and then show how recent derandomization techniques can be used to make it deterministic. The algorithm is an exact learning algorithm, but one where the equivalance query hypotheses and the final output are general (not necessarily k-term) DNF formulas.For the special case of 2-term DNF formulas, we give a simpler version of our algorithm that uses at most 4n + 2 total membership and equivalence queries.