  Boosting combines weak classifiers to form highly accurate predictors.
Although the case of binary classification is well understood, in the
multiclass setting, the "correct" requirements on the weak classifier, or the
notion of the most efficient boosting algorithms are missing. In this paper, we
create a broad and general framework, within which we make precise and identify
the optimal requirements on the weak-classifier, as well as design the most
effective, in a certain sense, boosting algorithms that assume such
requirements.
