Shrinking the Tube: A New Support Vector Regression Algorithm

Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)

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Bernhard Schölkopf, Peter Bartlett, Alex Smola, Robert C. Williamson


A new algorithm for Support Vector regression is described. For a priori chosen 1/, it automatically adjusts a flexible tube of minimal radius to the data such that at most a fraction 1/ of the data points lie outside. More(cid:173) over, it is shown how to use parametric tube shapes with non-constant radius. The algorithm is analysed theoretically and experimentally.