Generalization error bounds for classifiers trained with interdependent data

Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)

Authors

Nicolas Usunier, Massih R. 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.