Communications of the ACM
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Circuit complexity and neural networks
Circuit complexity and neural networks
On the Size of Weights for Threshold Gates
SIAM Journal on Discrete Mathematics
How fast can a threshold gate learn?
Proceedings of a workshop on Computational learning theory and natural learning systems (vol. 1) : constraints and prospects: constraints and prospects
A new algorithm for minimizing convex functions over convex sets
Mathematical Programming: Series A and B
Oracles and queries that are sufficient for exact learning
Journal of Computer and System Sciences
Exact Learning of Formulas in Parallel
Machine Learning
A composition theorem for learning algorithms with applications to geometric concept classes
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Solving convex programs by random walks
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Perceptron, Winnow, and PAC Learning
SIAM Journal on Computing
Machine Learning
Machine Learning
PAC = PAExact and Other Equivalent Models in Learning
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
Exploring Learnability between Exact and PAC
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
A simple polynomial-time rescaling algorithm for solving linear programs
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
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We study exact learning of halfspaces from equivalence queries. The algorithm uses an oracle RCH that returns a random consistent hypothesis to the counterexamples received from the equivalence query oracle. We use the RCH oracle to give a new polynomial time algorithm for exact learning halfspaces from majority of halfspaces and show that its query complexity is less (by some constant factor) than the best known algorithm that learns halfspaces from halfspaces.