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https://hdl.handle.net/2440/55225
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Type: | Journal article |
Title: | Penalised spline support vector classifiers: computational issues |
Author: | Ormerod, J. Wand, M. Koch, I. |
Citation: | Computational Statistics, 2008; 23(4):623-641 |
Publisher: | Physica-Verlag GMBH & Co |
Issue Date: | 2008 |
ISSN: | 0943-4062 1613-9658 |
Statement of Responsibility: | John T. Ormerod, M. P. Wand and Inge Koch |
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. |
Keywords: | Additive models Interior point methods Low dimensional structure Low-rank Kernels Semiparametric regression Support vector machines |
DOI: | 10.1007/s00180-007-0102-8 |
Published version: | http://dx.doi.org/10.1007/s00180-007-0102-8 |
Appears in Collections: | Aurora harvest Mathematical Sciences publications |
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