The University of Southampton
University of Southampton Institutional Repository

Signal theory for SVM kernel design with applications to parameter estimation and sequence kernels

Signal theory for SVM kernel design with applications to parameter estimation and sequence kernels
Signal theory for SVM kernel design with applications to parameter estimation and sequence kernels
Fourier-based regularisation is considered for the support vector machine (SVM) classification problem over absolutely integrable loss functions. By considering the problem in a signal theory setting, we show that a principled and finite kernel hyperparameter search space can be discerned a priori by using the sinc kernel. The training and validation phase required to optimise the SVM can thus be limited to this hyperparameter search space. The method is adapted to a recently proposed max sequence kernel such that positive semi-definiteness, and so convergence, is guaranteed
0925-2312
15-22
Nelson, J. D. B.
3bef57a7-4c0e-4501-bea7-0e528bcd64a2
Damper, R. I.
6e0e7fdc-57ec-44d4-bc0f-029d17ba441d
Gunn, S. R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Guo, B.
6c4581ac-e3e3-4002-992a-3abb7776ec5d
Nelson, J. D. B.
3bef57a7-4c0e-4501-bea7-0e528bcd64a2
Damper, R. I.
6e0e7fdc-57ec-44d4-bc0f-029d17ba441d
Gunn, S. R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Guo, B.
6c4581ac-e3e3-4002-992a-3abb7776ec5d

Nelson, J. D. B., Damper, R. I., Gunn, S. R. and Guo, B. (2008) Signal theory for SVM kernel design with applications to parameter estimation and sequence kernels. Neurocomputing, 72 (1-3), 15-22. (doi:10.1016/j.neucom.2008.01.034).

Record type: Article

Abstract

Fourier-based regularisation is considered for the support vector machine (SVM) classification problem over absolutely integrable loss functions. By considering the problem in a signal theory setting, we show that a principled and finite kernel hyperparameter search space can be discerned a priori by using the sinc kernel. The training and validation phase required to optimise the SVM can thus be limited to this hyperparameter search space. The method is adapted to a recently proposed max sequence kernel such that positive semi-definiteness, and so convergence, is guaranteed

Other
paper.ps - Other
Download (575kB)

More information

Published date: 2008
Organisations: Electronic & Software Systems, Southampton Wireless Group

Identifiers

Local EPrints ID: 265121
URI: http://eprints.soton.ac.uk/id/eprint/265121
ISSN: 0925-2312
PURE UUID: 77c5e2db-959a-421b-b390-61e370b14f06

Catalogue record

Date deposited: 29 Jan 2008 08:22
Last modified: 14 Mar 2024 08:03

Export record

Altmetrics

Contributors

Author: J. D. B. Nelson
Author: R. I. Damper
Author: S. R. Gunn
Author: B. Guo

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×