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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Hong, X., Chen, S., & Harris, C. J. (2006). Fast kernel classifier construction using orthogonal forward selection to minimise leave-one-out misclassification rate. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4113 LNCS-I, pp. 106–114). Springer Verlag. https://doi.org/10.1007/11816157_11
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