article
Support Vector Regression Machines
Harris Druckera, Christopher J. C. Burgesb, Linda Kaufmanc, Alex J. Smolac, Vladimir N. Vapnikd
- aDepartment of Electronic Engineering, Bell Labs and Monmouth University, NJ#TAB#
- bBellLabs#TAB#
- cBELL LABORATORIES
- dAT&T-Labs
1996-12-03 4,653 citations8 references
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Abstract
A new regression technique based on concept of support vectors is introduced. We compare support vector regression 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 input space. This is a longer version of the paper appear in Advances in Neural Processing Systems 9 (proceedings of the 1996 conference)