• Corpus ID: 743542

Support Vector Regression Machines

@inproceedings{Drucker1996SupportVR,
  title={Support Vector Regression Machines},
  author={Harris Drucker and Christopher J. C. Burges and Linda Kaufman and Alex Smola and Vladimir Naumovich Vapnik},
  booktitle={NIPS},
  year={1996}
}
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. 

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