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}, |
| Keywords: | |
| Projects: |
Idiap |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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