PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Batch and online learning algorithms for nonconvex Neyman-Pearson classification
Gilles Gasso, Aristidis Pappaioannou, Marina Spivak and Leon Bottou
ACM Transaction on Intelligent System and Technologies Volume 2, Number 3, 2011. ISSN 2157-6904

Abstract

We describe and evaluate two algorithms for Neyman-Pearson (NP) classification problem which has been recently shown to be of a particular importance for bipartite ranking problems. NP classification is a nonconvex problem involving a constraint on false negatives rate. We investigated batch algorithm based on DC programming and stochastic gradient method well suited for large scale datasets. Empirical evidences illustrate the potential of the proposed methods

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7568
Deposited By:Gilles Gasso
Deposited On:17 March 2011