Convex Cost Functions for Support Vector Regression

Smola, AJ; Schölkopf, B; Müller, K-R

doi:10.1007%2F978-1-4471-1599-1_10.pdf

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Convex Cost Functions for Support Vector Regression

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Smola, A., Schölkopf, B., & Müller, K.-R. (1998). Convex Cost Functions for Support Vector Regression. In L. Niklasson, M. Bodén, & T. Ziemke (Eds.), ICANN 98: 8th International Conference on Artificial Neural Networks, Skövde, Sweden, 2–4 September 1998 (pp. 99-104). London, UK: Springer.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-E94E-D

Abstract

The concept of Support Vector 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.