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Abstract:
We develop an intuitive geometric framework for support vector
regression (SVR). By examining when ε-tubes exist, we
show that SVR can be regarded as a classification problem in the
dual space. Hard and soft ε-tubes are constructed by
separating the convexor reduced convex hulls respectively of the
training data with the response variable shifted up and down by
ε. 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 ε-tube. In
the proposed approach the effects of the choices of all
parameters become clear geometrically.
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