Penalised spline support vector classifiers: computational issues

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dc.contributor.author Ormerod, JT
dc.contributor.author Wand, M
dc.contributor.author Koch, I
dc.date.accessioned 2012-02-02T04:04:17Z
dc.date.issued 2008-01
dc.identifier.citation Computational Statistics, 2008, 23 (4), pp. 623 - 641
dc.identifier.issn 0943-4062
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.
dc.publisher Physica-Verlag Gmbh & Co
dc.relation.isbasedon 10.1007/s00180-007-0102-8
dc.subject Additive models - Interior point methods - Low-dimensional structure - Low-rank Kernels - Semiparametric regression - Support vector machines, Statistics & Probability
dc.subject Additive models - Interior point methods - Low-dimensional structure - Low-rank Kernels - Semiparametric regression - Support vector machines; Statistics & Probability
dc.title Penalised spline support vector classifiers: computational issues
dc.type Journal Article
dc.parent Computational Statistics
dc.journal.volume 4
dc.journal.volume 23
dc.journal.number 4 en_US
dc.publocation Heidelberg, Germany en_US
dc.publocation Sydney, Australia
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.conference ConnectED: International Conference on Design Education
dc.for 0104 Statistics
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.date.activity 2010-06-28
dc.location.activity en_US
dc.location.activity Sydney, Australia
dc.description.keywords Additive models - Interior point methods - Low-dimensional structure - Low-rank Kernels - Semiparametric regression - Support vector machines en_US
dc.description.keywords design enquiry
dc.description.keywords practice based research
dc.description.keywords practice led research
dc.description.keywords methodology
dc.description.keywords Dreyfus model of expertise.
dc.description.keywords Additive models - Interior point methods - Low-dimensional structure - Low-rank Kernels - Semiparametric regression - Support vector machines
dc.description.keywords Additive models - Interior point methods - Low-dimensional structure - Low-rank Kernels - Semiparametric regression - Support vector machines
dc.description.keywords Additive models - Interior point methods - Low-dimensional structure - Low-rank Kernels - Semiparametric regression - Support vector machines
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Science
pubs.organisational-group /University of Technology Sydney/Faculty of Science/School of Mathematical Sciences


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