We provide ecient 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 classication noise setting.
['Nina Balcan', 'Christopher Berlind', 'Steven Ehrlich', 'Yingyu Liang']
Efficient Semi-supervised and Active Learning of Disjunctions