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A signal theory approach to support vector classification: the sinc kernel

Nelso, James D.B., Damper, Robert I., Gunn, Steve R. and Guo, Baofeng (2009) A signal theory approach to support vector classification: the sinc kernel Neural Networks, 22, (1), pp. 49-57. (doi:10.1016/j.neunet.2008.09.016). (PMID:19118976).

Record type: Article


Fourier-based regularisation is considered for the support vector machine classification problem over absolutely integrable loss functions. By invoking the modest assumption that the decision function belongs to a Paley–Wiener space, it is shown that the classification problem can be developed in the context of signal theory. Furthermore, by employing the Paley–Wiener reproducing kernel, namely the sinc function, it is shown that a principled and finite kernel hyper-parameter search space can be discerned, a priori. Subsequent simulations performed on a commonly-available hyperspectral image data set reveal that the approach yields results that surpass state-of-the-art benchmarks.

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e-pub ahead of print date: 12 November 2008
Published date: January 2009
Keywords: hyperspectral imaging, parameter estimation, regularisation, reproducing kernel hilbert spaces, sequency analysis, signal theory, sinc kernel, support vector machines
Organisations: Electronic & Software Systems, Southampton Wireless Group


Local EPrints ID: 266637
PURE UUID: ae1f3973-fd33-4d42-b075-1347fcca20c1

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Date deposited: 08 Sep 2008 20:16
Last modified: 18 Jul 2017 07:14

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Author: James D.B. Nelso
Author: Robert I. Damper
Author: Steve R. Gunn
Author: Baofeng Guo

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