Signal theory for SVM kernel design with applications to parameter estimation and sequence kernels

Authors:
J. D. B. Nelson;R. I. Damper;S. R. Gunn;B. Guo
Affiliations:
Information: Signals, Images, Systems (ISIS) Research Group, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK;Information: Signals, Images, Systems (ISIS) Research Group, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK;Information: Signals, Images, Systems (ISIS) Research Group, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK;Information: Signals, Images, Systems (ISIS) Research Group, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
Venue:
Neurocomputing
Year:
2008

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Abstract

Fourier-based regularisation is considered for the support vector machine (SVM) classification problem over absolutely integrable loss functions. By considering the problem in a signal theory setting, we show that a principled and finite kernel hyperparameter search space can be discerned a priori by using the sinc kernel. The training and validation phase required to optimise the SVM can thus be limited to this hyperparameter search space. The method is adapted to a recently proposed max sequence kernel such that positive semi-definiteness, and so convergence, is guaranteed.