Efficient Semi-supervised and Active Learning of Disjunctions
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(1):633-641, 2013.
We provide efficient algorithms for learning disjunctions in the semi-supervised setting under a natural regularity assumption introduced by (Balcan & Blum, 2005). We prove bounds on the sample complexity of our algorithms under a mild restriction on the data distribution. We also give an active learning algorithm with improved sample complexity and extend all our algorithms to the random classification noise setting.