Generalization error bounds for classifiers trained with interdependent data

Nicolas Usunier, Massih-Reza Amini, Patrick Gallinari

Abstract: In this paper we propose a general framework to study the generalization properties of binary classifiers trained with data which may be depen- dent, but are deterministically generated upon a sample of independent examples. It provides generalization bounds for binary classification and some cases of ranking problems, and clarifies the relationship between these learning tasks.