Local Linear Approximation for Kernel Methods: The Railway Kernel

  • Alberto Muñoz
  • Javier González
  • Isaac Martín de Diego
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)


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.


Support Vector Machine Kernel Method Linear Kernel Linear Support Vector Machine Local Linear Approximation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alberto Muñoz
    • 1
  • Javier González
    • 1
  • Isaac Martín de Diego
    • 2
  1. 1.University Carlos III de MadridGetafeSpain
  2. 2.University Rey Juan CarlosMóstolesSpain

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