General cost functions for support vector regression.

Smola, AJ; Schölkopf, B; Müller, K-R

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General cost functions for support vector regression.

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http://alex.smola.org/papers/1998/SmoSchMul98.pdf
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Smola, A., Schölkopf, B., & Müller, K.-R. (1998). General cost functions for support vector regression. In T. Downs, M. Frean, & M. Gallagher (Eds.), Ninth Australian Conference on Neural Networks (ACNN 1998) (pp. 79-83). St. Lucia.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-E952-1

Abstract

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.