  The problem of devising learning strategies for discrete losses (e.g.,
multilabeling, ranking) is currently addressed with methods and theoretical
analyses ad-hoc for each loss. In this paper we study a least-squares framework
to systematically design learning algorithms for discrete losses, with
quantitative characterizations in terms of statistical and computational
complexity. In particular we improve existing results by providing explicit
dependence on the number of labels for a wide class of losses and faster
learning rates in conditions of low-noise. Theoretical results are complemented
with experiments on real datasets, showing the effectiveness of the proposed
general approach.
