  Active learning is typically used to label data, when the labeling process is
expensive. Several active learning algorithms have been theoretically proved to
perform better than their passive counterpart. However, these algorithms rely
on some assumptions, which themselves contain some specific parameters. This
paper adresses the problem of adaptive active learning in a nonparametric
setting with minimal assumptions. We present a novel algorithm that is valid
under more general assumptions than the previously known algorithms, and that
can moreover adapt to the parameters used in these assumptions. This allows us
to work with a larger class of distributions, thereby avoiding to exclude
important densities like gaussians. Our algorithm achieves a minimax rate of
convergence, and therefore performs almost as well as the best known
non-adaptive algorithms.
