  We consider the problem of adaptation to the margin in binary classification.
We suggest a penalized empirical risk minimization classifier that adaptively
attains, up to a logarithmic factor, fast optimal rates of convergence for the
excess risk, that is, rates that can be faster than n^{-1/2}, where n is the
sample size. We show that our method also gives adaptive estimators for the
problem of edge estimation.
