  In this paper we introduce an alternative localization approach for binary
classification that leads to a novel complexity measure: fixed points of the
local empirical entropy. We show that this complexity measure gives a tight
control over complexity in the upper bounds. Our results are accompanied by a
novel minimax lower bound that involves the same quantity. In particular, we
practically answer the question of optimality of ERM under bounded noise for
general VC classes.
