logo Idiap Research Institute        
 [BibTeX] [Marc21]
A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification
Type of publication: Conference paper
Citation: grandvalet:nips:2005
Booktitle: Advances in Neural Information Processing Systems, NIPS 15
Year: 2005
Note: IDIAP-RR 05-26
Crossref: grandvalet:rr05-26:
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 when decisions result in unequal losses. Experiments on an unbalanced classification loss show improvements over state-of-the-art procedures.
Userfields: ipdmembership={learning},
Projects Idiap
Authors Grandvalet, Yves
MariƩthoz, Johnny
Bengio, Samy
Added by: [UNK]
Total mark: 0
  • grandvalet-nips-2005.pdf
  • grandvalet-nips-2005.ps.gz