This work describes the application of the Maximal Discrepancy (MD) criterion to the process of hyperparameter setting in
SVMs and points out the advantages of such an approach over existing theoretical and practical frameworks.
The resulting theoretical predictions are compared with a k-fold cross-validation empirical method on some benchmark datasets showing that the MD technique can be used for automatic
SVM model selection.