A signal theory approach to support vector classification: The sinc kernel

作者：Nelson James D B; Damper Robert I^*; Gunn Steve R; Guo Baofeng

来源：Neural Networks, 2009, 22(1): 49-57.

DOI：10.1016/j.neunet.2008.09.016

• Hyperspecral imaging
• Parameter estimation
• Regularisation
• Reproducing kernel Hilbert spaces
• Sequency analysis
• Signal theory
• Sinc kernel
• Support vector machines

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.

• 出版日期2009-1