  We present a new active learning algorithm based on nonparametric estimators
of the regression function. Our investigation provides probabilistic bounds for
the rates of convergence of the generalization error achievable by proposed
method over a broad class of underlying distributions. We also prove minimax
lower bounds which show that the obtained rates are almost tight.
