A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification
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 when decisions result in unequal losses. Experiments on an unbalanced classification loss show improvements over state-of-the-art procedures.
- URL: http://publications.idiap.ch/downloads/papers/2005/grandvalet-nips-2005.pdf
- Related documents: http://publications.idiap.ch/index.php/publications/showcite/grandvalet:rr05-26
Record created on 2006-03-10, modified on 2016-08-08