Communications of the ACM
A hard-core predicate for all one-way functions
STOC '89 Proceedings of the twenty-first 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
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
An improved boosting algorithm and its implications on learning complexity
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Learning read-once formulas with queries
Journal of the ACM (JACM)
Small-bias probability spaces: efficient constructions and applications
SIAM Journal on Computing
Learning decision trees using the Fourier spectrum
SIAM Journal on Computing
An efficient membership-query algorithm for learning DNF with respect to the uniform distribution
Journal of Computer and System Sciences
Attribute-efficient learning in query and mistake-bound models
Journal of Computer and System Sciences
Modern Cryptography, Probabilistic Proofs, and Pseudorandomness
Modern Cryptography, Probabilistic Proofs, and Pseudorandomness
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Introduction to Coding Theory
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
On approximating weighted sums with exponentially many terms
Journal of Computer and System Sciences
Almost tight upper bound for finding Fourier coefficients of bounded pseudo-Boolean functions
Journal of Computer and System Sciences
Hi-index | 0.00 |
An efficient algorithm exists for learning disjunctive normal form (DNF) expressions in the uniformdistribution PAC learning model with membership queries (J. Comput. System Sci. 55 (1997) 414), but in practice the algorithm can only be applied to small problems. We present several modifications to the algorithm that substantially improve its asymptotic efficiency. First, we show how to significantly improve the time and sample complexity of a key subprogram, resulting in similar improvements in the bounds on the overall DNF algorithm. We also apply known methods to convert the resulting algorithm to an attribute efficient algorithm. Furthermore, we develop a technique for lower bounding the sample size required for PAC learning with membership queries under a fixed distribution and apply this technique to produce a lower bound on the number of membership queries needed for the uniform-distribution DNF learning problem. Finally, we present a learning algorithm for DNF that is attribute efficient in its use of random bits.