The Leave-One-Out Kernel

  • Koji Tsuda
  • Motoaki Kawanabe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2415)

Abstract

Recently, several attempts have been made for deriving data-dependent kernels from distribution estimates with parametric models (e.g. the Fisher kernel). In this paper, we propose a new kernel derived from any distribution estimators, parametric or nonparametric. This kernel is called the Leave-one-out kernel (i.e. LOO kernel), because the leave-one-out process plays an important role to compute this kernel. We will show that, when applied to a parametric model, the LOO kernel converges to the Fisher kernel asymptotically as the number of samples goes to infinity.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Koji Tsuda
    • 1
  • Motoaki Kawanabe
    • 2
  1. 1.AIST CBRCJapan
  2. 2.Fraunhofer FIRSTBerlinGermany

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