Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Proceedings of the 10th International Workshop on Artificial Intelligence & Statistics, 2005 A Uniform Convergence Bound for the Area Under the ROC Curve
 

Summary: Proceedings of the 10th International Workshop on Artificial Intelligence & Statistics, 2005
A Uniform Convergence Bound for the Area Under the ROC Curve
Shivani Agarwal, Sariel Har-Peled and Dan Roth
Department of Computer Science
University of Illinois at Urbana-Champaign
201 N. Goodwin Avenue
Urbana, IL 61801, USA
{sagarwal,sariel,danr}@cs.uiuc.edu
Abstract
The area under the ROC curve (AUC) has been
advocated as an evaluation criterion for the bi-
partite ranking problem. We study uniform con-
vergence properties of the AUC; in particular, we
derive a distribution-free uniform convergence
bound for the AUC which serves to bound the
expected accuracy of a learned ranking function
in terms of its empirical AUC on the training se-
quence from which it is learned. Our bound is ex-
pressed in terms of a new set of combinatorial pa-
rameters that we term the bipartite rank-shatter

  

Source: Agarwal, Shivani - Department of Computer Science and Automation, Indian Institute of Science, Bangalore

 

Collections: Computer Technologies and Information Sciences