Rademacher Complexity and Structural Risk Minimization: An Application to Human Gene Expression Datasets

In this paper, we target the problem of model selection for Support Vector Classifiers through in–sample methods, which are particularly appealing in the small–sample regime, i.e. when few high–dimensional patterns are available. In particular, we describe the application of a trimmed hinge loss function to Rademacher Complexity and Maximal Discrepancy based in–sample approaches. We also show that the selected classifiers outperform the ones obtained with other state-of-the-art in-sample and out–of–sample model selection techniques in classifying Human Gene Expression datasets.