Penalised spline support vector classifiers: computational issues

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dc.contributor.author Ormerod, John en_US
dc.contributor.author Koch, Inge en_US
dc.contributor.author Wand, Matt en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-02-02T04:04:17Z
dc.date.available 2012-02-02T04:04:17Z
dc.date.issued 2008 en_US
dc.identifier 2010000455 en_US
dc.identifier.citation Ormerod John, Wand Matt, and Koch Inge 2008, 'Penalised spline support vector classifiers: computational issues', Physica-Verlag Gmbh & Co, vol. 23, no. 4, pp. 623-641. en_US
dc.identifier.issn 0943-4062 en_US
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/14483
dc.description.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. en_US
dc.language en_US
dc.publisher Physica-Verlag Gmbh & Co en_US
dc.relation.isbasedon http://dx.doi.org/10.1007/s00180-007-0102-8 en_US
dc.title Penalised spline support vector classifiers: computational issues en_US
dc.parent Computational Statistics en_US
dc.journal.volume 23 en_US
dc.journal.number 4 en_US
dc.publocation Heidelberg, Germany en_US
dc.identifier.startpage 623 en_US
dc.identifier.endpage 641 en_US
dc.cauo.name SCI.Mathematical Sciences en_US
dc.conference Verified OK en_US
dc.for 010400 en_US
dc.personcode 0000064995 en_US
dc.personcode 110509 en_US
dc.personcode 0000065002 en_US
dc.percentage 100 en_US
dc.classification.name Statistics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Additive models - Interior point methods - Low-dimensional structure - Low-rank Kernels - Semiparametric regression - Support vector machines en_US


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