  Different types of training data have led to numerous schemes for supervised
classification. Current learning techniques are tailored to one specific scheme
and cannot handle general ensembles of training data. This paper presents a
unifying framework for supervised classification with general ensembles of
training data, and proposes the learning methodology of generalized robust risk
minimization (GRRM). The paper shows how current and novel supervision schemes
can be addressed under the proposed framework by representing the relationship
between examples at test and training via probabilistic transformations. The
results show that GRRM can handle different types of training data in a unified
manner, and enable new supervision schemes that aggregate general ensembles of
training data.
