Abstract(s) :
(Anglais) There is a large body of work on convergence rates either in passive or active learning. Here we first
outline some of the main results that have been obtained, more specifically in a nonparametric setting
under assumptions about the smoothness of the regression function (or the boundary between classes) and the margin noise. We discuss the relative merits
of these underlying assumptions by putting active learning in perspective with recent work on passive
learning. We design an active learning algorithm with a rate of convergence better than
in passive learning, using a particular smoothness assumption customized for k-nearest neighbors. Unlike previous active learning algorithms, we use a smoothness assumption that provides a dependence on the marginal distribution of the instance space. Additionally, our algorithm avoids the strong density assumption that supposes the existence
of the density function of the marginal distribution of the instance space and is therefore more generally applicable.