Hyperparameter design criteria for support vector classifiers

Abstract The design of a support vector machine (SVM) consists in tuning a set of hyperparameter quantities, and requires an accurate prediction of the classifier's generalization performance. The paper describes the application of the maximal-discrepancy criterion to the hyperparameter-setting process, and points out the advantages of such an approach over existing theoretical frameworks. The resulting theoretical predictions are then compared with the k -fold cross-validation empirical method, which probably is the current best-performing approach to the SVM design problem. Experimental results on a wide range of real-world testbeds prove out that the features of the maximal-discrepancy method can notably narrow the gap that so far has separated theoretical and empirical estimates of a classifier's generalization error.