Abstract
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.
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© 2001 Springer-Verlag Berlin Heidelberg
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Graf, A.B., Borer, S. (2001). Normalization in Support Vector Machines. In: Radig, B., Florczyk, S. (eds) Pattern Recognition. DAGM 2001. Lecture Notes in Computer Science, vol 2191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45404-7_37
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DOI: https://doi.org/10.1007/3-540-45404-7_37
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42596-0
Online ISBN: 978-3-540-45404-5
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