The concept of Support Vetor Regression is extended to a more general class of convex cost functions. It is shown how the resulting convex constrained optimization problems can be efficiently solved by a Primal-Dual Interior Point path following method. Both computational feasibility and improvement of estimation is demonstrated in the experiments.
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