Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)
Bernhard Schölkopf, Peter L. Bartlett, Alex J. 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.