English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Classification in a Normalized Feature Space using Support Vector Machines

MPS-Authors
/persons/resource/persons83943

Graf,  ABA
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Graf, A., Smola, A., & Borer, S. (2003). Classification in a Normalized Feature Space using Support Vector Machines. IEEE Transactions on Neural Networks, 14(3), 597-605. doi:10.1109/TNN.2003.811708.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-DC7D-C
Abstract
This paper discusses classification using support vector machines in a normalized feature space. We consider both normalization in input space and in feature space. Exploiting the fact that in this setting all points lie on the surface of a unit hypersphere we replace the optimal separating hyperplane by one that is symmetric in its angles, leading to an improved estimator. Evaluation of these considerations is done in numerical experiments on two real-world datasets. The stability to noise of this offset correction is subsequently investigated as well as its optimality.