Shrinking the Tube: A New Support Vector Regression Algorithm
Abstract
A new algorithm for Support Vector regression is described. For a priori chosen 1/, it automatically adjusts a flexible tube of minimal radius to the data such that at most a fraction 1/ of the data points lie outside. More(cid:173) over, it is shown how to use parametric tube shapes with non-constant radius. The algorithm is analysed theoretically and experimentally.
Cite
Text
Schölkopf et al. "Shrinking the Tube: A New Support Vector Regression Algorithm." Neural Information Processing Systems, 1998.Markdown
[Schölkopf et al. "Shrinking the Tube: A New Support Vector Regression Algorithm." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/scholkopf1998neurips-shrinking/)BibTeX
@inproceedings{scholkopf1998neurips-shrinking,
title = {{Shrinking the Tube: A New Support Vector Regression Algorithm}},
author = {Schölkopf, Bernhard and Bartlett, Peter L. and Smola, Alex J. and Williamson, Robert C.},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {330-336},
url = {https://mlanthology.org/neurips/1998/scholkopf1998neurips-shrinking/}
}