Fast kernel classifier construction using orthogonal forward selection to minimise leave-one-out misclassification rate

Authors:
X. Hong;S. Chen;C. J. Harris
Affiliations:
Department of Cybernetics, University of Reading, Reading, U.K.;School of Electronics and Computer Science, University of Southampton, Southampton, U.K.;School of Electronics and Computer Science, University of Southampton, Southampton, U.K.
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

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Abstract

We propose a simple yet computationally efficient construction algorithm for two-class kernel classifiers. In order to optimise classifier’s generalisation capability, an orthogonal forward selection procedure is used to select kernels one by one by minimising the leave-one-out (LOO) misclassification rate directly. It is shown that the computation of the LOO misclassification rate is very efficient owing to orthogonalisation. Examples are used to demonstrate that the proposed algorithm is a viable alternative to construct sparse two-class kernel classifiers in terms of performance and computational efficiency.