In many multi-class classification tasks, the potential set of classes is vast (think face detection or speaker identification).
Researchers often design and evaluate the classifiers on a subset from the population of classes to which they will be applied.
In this talk, I argue that it may be useful to model the observed set of classes as a random sample from a population.
As the main...