Communications of the ACM
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Learning Nested Differences of Intersection-Closed Concept Classes
Machine Learning
SIAM Journal on Computing
Predicting {0, 1}-functions on randomly drawn points
Information and Computation
Constraints, consistency and closure
Artificial Intelligence
A Dichotomy Theorem for Learning Quantified Boolean Formulas
Machine Learning - Special issue: computational learning theory, COLT '97
On-line learning with malicious noise and the closure algorithm
Annals of Mathematics and Artificial Intelligence
Machine Learning
Machine Learning
Exploring Learnability between Exact and PAC
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Learnability of quantified formulas
Theoretical Computer Science
The computational complexity of quantified constraint satisfaction
The computational complexity of quantified constraint satisfaction
Quantified constraint satisfaction, maximal constraint languages, and symmetric polymorphisms
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
CSL'05 Proceedings of the 19th international conference on Computer Science Logic
Tractability and Learnability Arising from Algebras with Few Subpowers
SIAM Journal on Computing
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Intersection-closed classes of concepts arise naturally in many contexts and have been intensively studied in computational learning theory. In this paper, we study intersection-closed classes that contain the concepts invariant under an operation satisfying a certain algebraic condition. We give a learning algorithm in the exact model with equivalence queries for such classes. This algorithm utilizes a novel encoding scheme, which we call a signature.