Generalization error bounds for classifiers trained with interdependent data

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.

Authors

• Nicolas Usunier
• Massih R. Amini University Grenoble Alps
• Patrick Gallinari

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