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We show how the SVM can be viewed as a maximum likelihood estimate of a class of probabilistic models. This model class can be viewed as a reparametrization of the SVM in a similar vein to the $ u$-SVM reparametrizing the original SVM. It is not discriminative, but has a non-uniform marginal. We illustrate the benefits of this new view by re-deriving and re-investigating two established SVM-related algorithms.
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