ML Anthology

Duality, Geometry, and Support Vector Regression

J. Bi, Kristin P. Bennett
NeurIPS 2001 pp. 593-600
/neurips/2001/bi2001neurips-duality/

Abstract

We develop an intuitive geometric framework for support vector regression (SVR). By examining when (cid:15)-tubes exist, we show that SVR can be regarded as a classi(cid:12)cation problem in the dual space. Hard and soft (cid:15)-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 (cid:15). 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 e(cid:11)ective (cid:15)-tube. In the proposed approach the e(cid:11)ects of the choices of all parameters become clear geometrically.

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Cite

Text

Bi and Bennett. "Duality, Geometry, and Support Vector Regression." Neural Information Processing Systems, 2001.

Markdown

[Bi and Bennett. "Duality, Geometry, and Support Vector Regression." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/bi2001neurips-duality/)

BibTeX

@inproceedings{bi2001neurips-duality,
  title     = {{Duality, Geometry, and Support Vector Regression}},
  author    = {Bi, J. and Bennett, Kristin P.},
  booktitle = {Neural Information Processing Systems},
  year      = {2001},
  pages     = {593-600},
  url       = {https://mlanthology.org/neurips/2001/bi2001neurips-duality/}
}