Nelso, James D.B., Damper, Robert I., Gunn, Steve R. and Guo, Baofeng
A signal theory approach to support vector classification: the sinc kernel
Neural Networks, 22, (1), . (doi:10.1016/j.neunet.2008.09.016). (PMID:19118976).
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.
|Digital Object Identifier (DOI):
||hyperspectral imaging, parameter estimation, regularisation, reproducing kernel hilbert spaces, sequency analysis, signal theory, sinc kernel, support vector machines
||Electronic & Software Systems, Southampton Wireless Group
|12 November 2008||e-pub ahead of print|
||08 Sep 2008 20:16
||17 Apr 2017 19:01
|Further Information:||Google Scholar|
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