%0 Conference Paper %1 Drucker1997Support %A Drucker, Harris %A Chris, %A Kaufman, Burges* L. %A Smola, Alex %A Vapnik, Vladimir %B Advances in Neural Information Processing Systems 9 %D 1997 %K regression svms %P 155--161 %T Support vector regression machines %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.5909 %V 9 %X 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 dimension&y of the input space.

@inproceedings{Drucker1997Support, 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 dimension\&y of the input space.}, added-at = {2008-12-09T03:00:06.000+0100}, author = {Drucker, Harris and Chris and Kaufman, Burges* L. and Smola, Alex and Vapnik, Vladimir}, biburl = {https://www.bibsonomy.org/bibtex/20546f7074d65988186502caabdb9d1d0/jamesh}, booktitle = {Advances in Neural Information Processing Systems 9}, citeulike-article-id = {3738213}, interhash = {31ca222b5e2197c9f565f381de767085}, intrahash = {0546f7074d65988186502caabdb9d1d0}, keywords = {regression svms}, pages = {155--161}, posted-at = {2008-12-03 00:03:01}, priority = {2}, timestamp = {2008-12-09T09:59:02.000+0100}, title = {Support vector regression machines}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.5909}, volume = 9, year = 1997 }