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01149 am a22001573u 4500 |
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266637 |
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|a Nelso, James D.B.
|e author
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|a Damper, Robert I.
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|a Gunn, Steve R.
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|a Guo, Baofeng
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|a A signal theory approach to support vector classification: the sinc kernel
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|c 2009-01.
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|z Get fulltext
|u https://eprints.soton.ac.uk/266637/1/nnt1906.pdf
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|a 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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