  We define a generalized likelihood function based on uncertainty measures and
show that maximizing such a likelihood function for different measures induces
different types of classifiers. In the probabilistic framework, we obtain
classifiers that optimize the cross-entropy function. In the possibilistic
framework, we obtain classifiers that maximize the interclass margin.
Furthermore, we show that the support vector machine is a sub-class of these
maximum-margin classifiers.
