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Conference Paper

Normalization in Support Vector Machines


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;

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Graf, A., & Borer, S. (2001). Normalization in Support Vector Machines. In B. Radig, & S. Florczyk (Eds.), Pattern Recognition: 23rd DAGM Symposium Munich, Germany, September 12–14, 2001 (pp. 277-282). Berlin, Germany: Springer.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-E212-F
This article deals with various aspects of normalization in the context of Support Vector Machines. We consider fist normalization of the vectors in the input space and point out the inherent limitations. A natural extension to the feature space is then represented by the kernel function normalization. A correction of the position of the Optimal Separating Hyperplane is subsequently introduced so as to suit better these normalized kernels. Numerical experiments finally evaluate the different approaches.