Penalised spline support vector classifiers: computational issues

Publisher:
Physica-Verlag Gmbh & Co
Publication Type:
Journal Article
Citation:
Computational Statistics, 2008, 23 (4), pp. 623 - 641
Issue Date:
2008-01
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We study computational issues for support vector classification with penalised spline kernels. We show that, compared with traditional kernels, computational times can be drastically reduced in large problems making such problems feasible for sample sizes as large as ~106. The optimisation technology known as interior point methods plays a central role. Penalised spline kernels are also shown to allow simple incorporation of low-dimensional structure such as additivity. This can aid both interpretability and performance.
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