Helmbold, D (contributor); Williamson, B (contributor) - 2001
In this paper we study a new restriction of the PAC learning framework, in which each label class is handled by an unsupervised learner that aims to fit an appropriate probability distribution to its own data. A hypothesis is derived by choosing, for any unlabeled instance, the label whose...