Penalised spline support vector classifiers: Computational issues

John T. Ormerod, M. P. Wand, Inge Koch

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)623-641
Number of pages19
JournalComputational Statistics
Volume23
Issue number4
DOIs
Publication statusPublished - 1 Oct 2008
Externally publishedYes

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