Communications of the ACM
On the learnability of Boolean formulae
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Computational limitations on learning from examples
Journal of the ACM (JACM)
Learning DNF under the uniform distribution in quasi-polynomial time
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Learning monotone Boolean functions by uniformly distributed examples
SIAM Journal on Computing
Learning DNF formulae under classes of probability distributions
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
On the Learnability of DNF Formulae
ICALP '88 Proceedings of the 15th International Colloquium on Automata, Languages and Programming
Learning k-Term Monotone Boolean Formulae
ALT '92 Proceedings of the Third Workshop on Algorithmic Learning Theory
Simple learning algorithms using divide and conquer
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
On the Fourier spectrum of monotone functions
Journal of the ACM (JACM)
On Learning Monotone DNF under Product Distributions
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
On learning monotone DNF under product distributions
Information and Computation
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Based on the uniform distribution PAC learning model, the learnability for monotone disjunctive normal form formulas with at most O(logn) terms (O(logn)-term MDNF) is investigated. Using the technique of restriction, an algorithm that learns O(logn)-term MDNF in polynomial time is given.