A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification
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 pro- cedure. This connection enables to derive a mapping from SVM scores to estimated posterior probabilities. Unlike previous proposals, the sug- gested mapping is interval-valued, providing a set of posterior probabil- ities compatible with each SVM score. This framework offers a new way to adapt the SVM optimization problem to unbalanced classifica- tion, when decisions result in unequal (asymmetric) losses. Experiments show improvements over state-of-the-art procedures.
Cite
Text
Grandvalet et al. "A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification." Neural Information Processing Systems, 2005.Markdown
[Grandvalet et al. "A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/grandvalet2005neurips-probabilistic/)BibTeX
@inproceedings{grandvalet2005neurips-probabilistic,
title = {{A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification}},
author = {Grandvalet, Yves and Mariethoz, Johnny and Bengio, Samy},
booktitle = {Neural Information Processing Systems},
year = {2005},
pages = {467-474},
url = {https://mlanthology.org/neurips/2005/grandvalet2005neurips-probabilistic/}
}