Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms

This paper discusses implementation issues related to the tuning of the hyperparameters of a support vector machine (SVM) with L-2 soft margin, for which the radius/margin bound is taken as the index to be minimized, and iterative techniques are employed for computing radius and margin. The implementation is shown to be feasible and efficient, even for large problems having more than 10000 support vectors.