Regularization approaches for support vector machines with applications to biomedical data


The support vector machine (SVM) is a widely used  machine learning tool for classification based on statistical learning theory. Given a set of training data, the SVM finds a hyperplane that separates two different classes of data points by the largest distance. While the standard form of SVM uses $L_2$-norm regularization, other  regularization approaches are particularly attractive for biomedical datasets where,   for example, sparsity and interpreability of the classifier's coefficient values are highly desired features. Therefore, in this paper we consider different types of regularization approaches for SVMs, and explore them in both synthetic and real biomedical datasets.