Duality, Geometry, and Support Vector Regression
Jinbo Bi, Kristin P. Bennett · The MIT Press eBooks · 2002
We develop an intuitive geometric framework for support vector regression (SVR). By examining when #-tubes exist, we show that SVR can be regarded a classification problem in the dual space. Hard and soft #-tubes are constructed by separating the convex or reduced convex hulls respectively of the training data with the response variable shifted up and down by #. A novel SVR model is proposed based on choosing the max-margin plane between the two shifted datasets. In the proposed approach the effects of the choices of all parameters become clear geometrically.