PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Sparse Probabilistic Classifiers
Romain Herault and Yves Grandvalet
In: ICML 2007, 20-24 June 2007, Oregon State University, Corvallis, OR, USA.

Abstract

The scores returned by support vector machines are often used as a confidence measures in the classification of new examples. However, there is no theoretical argument sustaining this practice. Thus, when classification uncertainty has to be assessed, it is safer to resort to classifiers estimating conditional probabilities of class labels. Here, we focus on the ambiguity in the vicinity of the boundary decision. We propose an adaptation of maximum likelihood estimation, instantiated on logistic regression. The model outputs proper conditional probabilities into a user-defined interval and is less precise elsewhere. The model is also sparse, in the sense that few examples contribute to the solution. The computational effciency is thus improved compared to logistic regression. Furthermore, preliminary experiments show improvements over standard logistic regression and performances similar to support vector machines.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3020
Deposited By:Romain Herault
Deposited On:09 August 2007