Penalised spline support vector classifiers: computational issues

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Show simple item record Ormerod, JT Wand, M Koch, I 2012-02-02T04:04:17Z 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.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.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 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 110509
dc.percentage 100 en_US Statistics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US en_US 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
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Science
utslib.copyright.status Closed Access 2015-04-15 12:17:09.805752+10
pubs.consider-herdc false
utslib.collection.history School of Mathematical Sciences (ID: 340)
utslib.collection.history Closed (ID: 3)

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