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).

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Description/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

Item Type: Article
Digital Object Identifier (DOI): doi:10.1016/j.neucom.2008.01.034
ISSNs: 0925-2312 (print)
Organisations: Electronic & Software Systems, Southampton Wireless Group
ePrint ID: 265121
Date :
Date Event
2008Published
Date Deposited: 29 Jan 2008 08:22
Last Modified: 17 Apr 2017 19:25
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/265121

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