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

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), pp. 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

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

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

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Date deposited: 29 Jan 2008 08:22
Last modified: 18 Jul 2017 07:29

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Contributors

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

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