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A. Smola, B. Sch\"olkopf, and K.-R. M\"uller (1998)
General cost functions for Support Vector Regression
In: Proc. of the Ninth Australian Conf. on Neural Networks, ed. by T. Downs and M. Frean and M. Gallagher, pp. 79 - 83, Brisbane, Australia, University of Queensland.
The concept of Support Vector Regression is extended to a more general class of convex cost functions. Moreover 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.
A slightly shorter version was published in: L. Niklasson and M. Boden and T. Ziemke (eds.), Proceedings of the 8th International Conference on Artificial Neural Networks, Springer Verlag, Perspectives in Neural Computing