On Tuning Hyper-Parameters of Multiclass Margin Classifiers
Abstract. The choice of hyper-parameters (e.g. kernel parameters) can significantly affect generalization performance of large margin classifiers. In this paper we are concerned with the problem of tuning these values in the case of multi-class problems that have been recast into a set of binary problems. We report several experimental results comparing independent and joint tuning of the hyper-parameters of the binary classifiers. Several different encoding strategies are explored, including error correcting output codes. Tuning was carried out by using a validation set and a newly introduced bound on the leave-one-out error.