Efficient cross-validation of kernel Fisher discriminant classifiers

Cawley, Gavin C. and Talbot, Nicola L. C. (2003) Efficient cross-validation of kernel Fisher discriminant classifiers. In: Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2003), 2003-04-23 - 2003-04-25.

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Mika et al. [1] introduce a non-linear formulation of the Fisher discriminant based the well-known "kernel trick", later shown to be equivalent to the Least-Squares Support Vector Machine [2, 3]. In this paper, we show that the cross-validation error can be computed very efficiently for this class of kernel machine, specifically that leave-one-out cross-validation can be performed with a computational complexity of only O(l3) operations (the same as that of the basic training algorithm), rather than the O(l4) of a direct implementation. This makes leave-one-out cross-validation a practical proposition for model selection in much larger scale applications of KFD classifiers.

Item Type: Conference or Workshop Item (Paper)
Faculty \ School: Faculty of Science > School of Computing Sciences

University of East Anglia > Faculty of Science > Research Groups > Computational Biology (subgroups are shown below) > Machine learning in computational biology
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Depositing User: Vishal Gautam
Date Deposited: 22 Jul 2011 13:11
Last Modified: 21 Jul 2021 23:41
URI: https://ueaeprints.uea.ac.uk/id/eprint/22156

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