Shrinking the Tube: A New Support Vector Regression Algorithm with Parametric Insensitive Model
Pei-Yi Hao · 2007
A new algorithm for support vector regression is described. For a priori chosen v, it automatically adjusts a flexible tube of arbitrary shape and minimal radius to include the data such that at most a fraction v 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.