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.
Nelson
James D B
JD
Information: Signals, Images, Systems Research Group, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK. jn@ecs.soton.ac.uk
Damper
Robert I
RI
Gunn
Steve R
SR
Guo
Baofeng
B
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted