Support vector regression machines Export

@inproceedings{citeulike:3738213, 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.}, author = {Drucker, Harris and Chris and Kaufman, Burges* L. and Smola, Alex and Vapnik, Vladimir}, booktitle = {Advances in Neural Information Processing Systems 9}, citeulike-article-id = {3738213}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.5909}, keywords = {regression, svms}, pages = {155--161}, posted-at = {2008-12-03 00:03:01}, priority = {2}, title = {Support vector regression machines}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.5909}, volume = {9}, year = {1997} }

TY - CONF ID - citeulike:3738213 L3 - citeulike-article-id:3738213 N2 - 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. EP - 161 TI - Support vector regression machines VL - 9 T2 - Advances in Neural Information Processing Systems 9 SP - 155 KW - regression KW - svms AU - Drucker, Harris AU - Chris AU - Kaufman, Burges* AU - Smola, Alex AU - Vapnik, Vladimir PY - 1997/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.5909 ER -