Support Vector Regression Machines
Abstract
A new regression technique based on Vapnik's concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space. On the basis of these experiments, it is expected that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimensionality of the input space.
Cite
Text
Drucker et al. "Support Vector Regression Machines." Neural Information Processing Systems, 1996.Markdown
[Drucker et al. "Support Vector Regression Machines." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/drucker1996neurips-support/)BibTeX
@inproceedings{drucker1996neurips-support,
title = {{Support Vector Regression Machines}},
author = {Drucker, Harris and Burges, Christopher J. C. and Kaufman, Linda and Smola, Alex J. and Vapnik, Vladimir},
booktitle = {Neural Information Processing Systems},
year = {1996},
pages = {155-161},
url = {https://mlanthology.org/neurips/1996/drucker1996neurips-support/}
}