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.


Year:
2005
Publisher:
IDIAP
Keywords:
Note:
Published in Advances in Neural Information Processing Systems, {NIPS} 15, 2005
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 Record created 2006-03-10, last modified 2018-03-17

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