Optimization of support vector machine hyperparameters using radius/margin bound

The paper presents the method of tuning the support vector machine hyperparameters by minimizing an estimate of the leave-one-out error known as radius/margin bound. The quality of the method, in terms of classification accuracy and generalization rate was tested against an exhaustive grid-search in hyperparameter space using a 2- dimensional Banana dataset.