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

A signal theory approach to support vector classification: the sinc kernel
A signal theory approach to support vector classification: the sinc kernel
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.
hyperspectral imaging, parameter estimation, regularisation, reproducing kernel hilbert spaces, sequency analysis, signal theory, sinc kernel, support vector machines
49-57
Nelso, James D.B.
3ac61cce-47fe-493a-8f0e-f4aef18c9cc5
Damper, Robert I.
6e0e7fdc-57ec-44d4-bc0f-029d17ba441d
Gunn, Steve R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Guo, Baofeng
e62b04c7-167b-45d9-a400-67a631861f24
Nelso, James D.B.
3ac61cce-47fe-493a-8f0e-f4aef18c9cc5
Damper, Robert I.
6e0e7fdc-57ec-44d4-bc0f-029d17ba441d
Gunn, Steve R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Guo, Baofeng
e62b04c7-167b-45d9-a400-67a631861f24

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), 49-57. (doi:10.1016/j.neunet.2008.09.016). (PMID:19118976)

Record type: Article

Abstract

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

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Local EPrints ID: 266637
URI: http://eprints.soton.ac.uk/id/eprint/266637
PURE UUID: ae1f3973-fd33-4d42-b075-1347fcca20c1

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Date deposited: 08 Sep 2008 20:16
Last modified: 14 Mar 2024 08:30

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Contributors

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

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