Supervised learning of classifiers via level set segmentation

A variational approach based on level set methods popular in image segmentation is presented for learning discriminative classifiers in general feature spaces. Nonlinear, nonparametric decision boundaries are obtained by minimizing an energy functional that incorporates a margin-based loss function. The class of level set contour decision boundaries is discussed in terms of the structural risk minimization principle. A variation on lscr₁ feature subset selection is developed. Use of level set classifiers as base learners for boosting is discussed.