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 practica] frameworks. The resulting theoretical predictions are compared with a k-fold'crossvalidation empírical method on sorne benchmark datasets showing that the MD technique can be used for automatic SVM model selection.
Automatic Hyperparameter Tuning for Support Vector Machines
ANGUITA, DAVIDE;RIDELLA, SANDRO;ZUNINO, RODOLFO
2002-01-01
Abstract
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 practica] frameworks. The resulting theoretical predictions are compared with a k-fold'crossvalidation empírical method on sorne benchmark datasets showing that the MD technique can be used for automatic SVM model selection.
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