PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Signal Theory for SVM Kernel Parameter Estimation
James Nelson, Bob Damper, Steve Gunn and Baofeng Guo
In: 2006 IEEE International Workshop on Machine Learning For Signal Processing, 6-8 Sept 2006, Maynooth, Ireland.

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Abstract

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 experiments, performed on a commonly available hyper-spectral image data set, reveal that the approach yields results that surpass state-of-the-art benchmarks.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:2917
Deposited By:Charanpal Dhanjal
Deposited On:23 November 2006

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