Generalisation Error Bounds for Classifiers Trained with Interdependent Data

Abstract : In this paper we propose a general framework to study the generalization properties of binary classifiers trained with data which may be dependent, 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.
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Conference papers
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Submitted on : Wednesday, March 15, 2017 - 2:19:13 PM
Last modification on : Thursday, March 21, 2019 - 1:04:54 PM

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  • HAL Id : hal-01490502, version 1

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Nicolas Usunier, Massih-Reza Amini, Patrick Gallinari. Generalisation Error Bounds for Classifiers Trained with Interdependent Data. NIPS 2005 - 18th International Conference on Neural Information Processing Systems, Dec 2005, Vancouver, Canada. pp.1369-1376. ⟨hal-01490502⟩

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