Shrinking the Tube: A New Support Vector Regression Algorithm Ikoma-shi, Nara 630-0101, Japan

Abstract:
A new algorithm for Support Vector regression is proposed. 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.