Shrinking the Tube: A New Support Vector Regression Algorithm

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

• Type: Conference
• Year: 1998
• Where: NIPS
• Authors: Bernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson
• Keywords: NIPS 1998, NIPS 2007, Parametric Tube Shapes, Priori Chosen, Support Vector Regression