Communications of the ACM
On the learnability of Boolean formulae
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Learning DNF under the uniform distribution in quasi-polynomial time
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Equivalence of models for polynomial learnability
COLT '88 Proceedings of the first annual workshop on Computational learning theory
An O(nlog log n) learning algorithm for DNF under the uniform distribution
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Learning read-once formulas with queries
Journal of the ACM (JACM)
The design and analysis of efficient learning algorithms
The design and analysis of efficient learning algorithms
On the hardness of approximating minimization problems
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
The complexity of learning formulas and decision trees that have restricted reads
The complexity of learning formulas and decision trees that have restricted reads
On using the Fourier transform to learn Disjoint DNF
Information Processing Letters
Journal of the ACM (JACM)
On the Learnability of Disjunctive Normal Form Formulas
Machine Learning
Constant depth circuits, Fourier transform, and learnability
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
Exact identification of circuits using fixed points of amplification functions
SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science
An efficient membership-query algorithm for learning DNF with respect to the uniform distribution
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
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
Learning DNF from random walks
Journal of Computer and System Sciences - Special issue: Learning theory 2003
APPROX '08 / RANDOM '08 Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques
Discrete Applied Mathematics
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In this paper, we give learning algorithms for two new subclass of DNF formulas: poly-disjoint One-read-once Monotone DNF; and Read-once Factorable Monotone DNF, which is a generalization of Read-once Monotone DNF formulas. Our result uses Fourier analysis to construct the terms of the target formula based on the Fourier coefficients corresponding to these terms. To facilitate this result, we give a novel theorem on the approximation of Read-once Factorable Monotone DNF formulas, in which we show that if a set of terms of the target formula have polynomially small mutually disjoint satisfying sets, then the set of terms can be approximated with small error by the greatest common factor of the set of terms. This approximation theorem may be of independent interest.