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Author Usunier, Nicolas ♦ Amini, Massih-Reza ♦ Gallinari, Patrick
Source CiteSeerX
Content type Text
Publisher MIT Press
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Binary Classifier ♦ Binary Classification ♦ General Framework ♦ Generalization Property ♦ Generalization Bound ♦ Interdependent Data ♦ Generalization Error ♦ Independent Example
Description 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. 1
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2006-01-01
Publisher Institution Advances in Neural Information Processing Systems 18 (NIPS 2005