  In this article, model selection via penalized empirical loss minimization in
nonparametric classification problems is studied. Data-dependent penalties are
constructed, which are based on estimates of the complexity of a small subclass
of each model class, containing only those functions with small empirical loss.
The penalties are novel since those considered in the literature are typically
based on the entire model class. Oracle inequalities using these penalties are
established, and the advantage of the new penalties over those based on the
complexity of the whole model class is demonstrated.
