Computing confidence intervals for the risk ofa SVM classifier through algorithmic inference
We reconsider in the Algorithmic Inference framework the accuracy of a Boolean function learnt from examples. This framework is specially suitable when the Boolean function is learnt through a Support Vector Machine, since (i) we know the number of support vectors really employed as an ancillary output of the learning procedure, and (ii) we can appreciate confidence intervals of misclassifying probability exactly in function of the cardinalitiy of these vectors. As a result we obtain confidence intervals that are up to an order narrower than those supplied in the literature, having a slight different meaning due to the different approach they come from, but the same operational function. We numerically check the covering of these intervals.