PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Generalisation Error Bounds for Classifiers Trained with Interdependent Data
Nicolas Usunier, Massih Amini and Patrick Gallinari
In: NIPS 2005, 3 Dec - 10 Dec 2005, Vancouver, Canada.


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

EPrint Type:Conference or Workshop Item (Spotlight)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1251
Deposited By:Massih Amini
Deposited On:28 November 2005