Signal Theory for SVM Kernel Parameter Estimation

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 sine function, it is shown that a principled and finite kernel hyper-parameter search space can be discerned, a priori. Subsequent experiments, performed on a commonly available hyper-spectral image data set, reveal that the approach yields results that surpass state-of-the-art benchmarks.