17 research outputs found
Reduction Scheme for Empirical Risk Minimization and Its Applications to Multiple-Instance Learning
In this paper, we propose a simple reduction scheme for empirical risk
minimization (ERM) that preserves empirical Rademacher complexity. The
reduction allows us to transfer known generalization bounds and algorithms for
ERM to the target learning problems in a straightforward way. In particular, we
apply our reduction scheme to the multiple-instance learning (MIL) problem, for
which generalization bounds and ERM algorithms have been extensively studied.
We show that various learning problems can be reduced to MIL. Examples include
top-1 ranking learning, multi-class learning, and labeled and complementarily
labeled learning. It turns out that, some of the generalization bounds derived
are, despite the simplicity of derivation, incomparable or competitive with the
existing bounds. Moreover, in some setting of labeled and complementarily
labeled learning, the algorithm derived is the first polynomial-time algorithm