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 PaleyWiener space, it is shown that the classification problem can be developed in the context of signal theory. Furthermore, by employing the PaleyWiener 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.
signal theory sinc kernel regularisation support vector machines hyperspectral imaging parameter estimation reproducing kernel hilbert spaces sequency analysis