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Duality, Geometry, and Support Vector Regression

By Jinbo Bi and Kristin P. Bennett

Abstract

We develop an intuitive geometric framework for support vector regression (SVR). By examining when ɛ-tubes exist, we show that SVR can be regarded as a classification problem in the dual space. Hard and soft ɛ-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 ɛ. A novel SVR model is proposed based on choosing the max-margin plane between the two shifted datasets. Maximizing the margin corresponds to shrinking the effective ɛ-tube. In the proposed approach the effects of the choices of all parameters become clear geometrically.

Publisher: MIT Press
Year: 2002
OAI identifier: oai:CiteSeerX.psu:10.1.1.135.2820
Provided by: CiteSeerX
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