nips nips2001 nips2001-62 nips2001-62-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: J. Bi, Kristin P. Bennett
Abstract: We develop an intuitive geometric framework for support vector regression (SVR). By examining when -tubes exist, we show that SVR can be regarded as 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. Maximizing the margin corresponds to shrinking the effective -tube. In the proposed approach the effects of the choices of all parameters become clear geometrically. 1
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