Abstract
In this paper we present a new kernel, the Railway Kernel, that works properly for general (nonlinear) classification problems, with the interesting property that acts locally as a linear kernel. In this way, we avoid potential problems due to the use of a general purpose kernel, like the RBF kernel, as the high dimension of the induced feature space. As a consequence, following our methodology the number of support vectors is much lower and, therefore, the generalizacion capability of the proposed kernel is higher than the obtained using RBF kernels. Experimental work is shown to support the theoretical issues.
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Keywords
- Support Vector Machine
- Kernel Method
- Linear Kernel
- Linear Support Vector Machine
- Local Linear Approximation
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© 2006 Springer-Verlag Berlin Heidelberg
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Muñoz, A., González, J., de Diego, I.M. (2006). Local Linear Approximation for Kernel Methods: The Railway Kernel. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2006. Lecture Notes in Computer Science, vol 4225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892755_97
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DOI: https://doi.org/10.1007/11892755_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46556-0
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