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
Boosting a weak learning algorithm by majority
COLT '90 Proceedings of the third 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
Circuits of the mind
Learning in the presence of finitely or infinitely many irrelevant attributes
Journal of Computer and System Sciences
Adaptive versus nonadaptive attribute-efficient learning
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Attribute-efficient learning in query and mistake-bound models
Journal of Computer and System Sciences
More efficient PAC-learning of DNF with membership queries under the uniform distribution
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
A neuroidal architecture for cognitive computation
Journal of the ACM (JACM)
Boosting and Hard-Core Set Construction
Machine Learning
Optimal Attribute-Efficient Learning of Disjunction, Parity and Threshold Functions
EuroCOLT '97 Proceedings of the Third European Conference on Computational Learning Theory
Exact Learning when Irrelevant Variables Abound
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Finding Relevant Variables in PAC Model with Membership Queries
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
Group Testing Problems with Sequences in Experimental Molecular Biology
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
Learning with Queries Corrupted by Classification Noise
ISTCS '97 Proceedings of the Fifth Israel Symposium on the Theory of Computing Systems (ISTCS '97)
On using extended statistical queries to avoid membership queries
The Journal of Machine Learning Research
Learning DNF from Random Walks
FOCS '03 Proceedings of the 44th Annual IEEE 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
| Hi-index | 0.00 |
We consider the problems of attribute-efficient PAC learning of two well-studied concept classes: parity functions and DNF expressions over {0,1}n. We show that attribute-efficient learning of parities with respect to the uniform distribution is equivalent to decoding high-rate random linear codes from low number of errors, a long-standing open problem in coding theory. An algorithm is said to use membership queries (MQs) non-adaptively if the points at which the algorithm asks MQs do not depend on the target concept. We give a deterministic and a fast randomized attribute-efficient algorithms for learning parities by non-adaptive MQs. Using our non-adaptive parity learning algorithm and a modification of Levin's algorithm for locating a weakly-correlated parity due to Bshouty et al., we give the first non-adaptive and attribute-efficient algorithm for learning DNF with respect to the uniform distribution. Our algorithm runs in time ${\tilde O}(ns^{4}/\epsilon)$ and uses ${\tilde O}(s^{4}/\epsilon)$ non-adaptive MQs where s is the number of terms in the shortest DNF representation of the target concept. The algorithm also improves on the best previous algorithm for learning DNF (of Bshouty et al.).