PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification
Yves Grandvalet, Johnny Mariéthoz and Samy Bengio
In: CAp'2006, June 2006, Tregastel, France.

Abstract

In this paper, we show that the hinge loss can be interpreted as the neg-log-likelihood of a semi-parametric model of posterior probabilities. From this point of view, SVMs represent the parametric component of a semi-parametric model fitted by a maximum a posteriori estimation procedure. This connection enables to derive a mapping from SVM scores to estimated posterior probabilities. Unlike previous proposals, the suggested mapping is interval-valued, providing a set of posterior probabilities compatible with each SVM score. This framework offers a new way to adapt the SVM optimization problem to unbalanced classification, when decisions result in unequal (asymmetric) losses. Experiments show improvements over state-of-the-art procedures.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:2592
Deposited By:Yves Grandvalet
Deposited On:22 November 2006