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

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 b...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2008-12, Vol.72 (1), p.15-22
Main Authors: Nelson, J.D.B., Damper, R.I., Gunn, S.R., Guo, B.
Format: Article
Language:English
Subjects:
Classification
Parameter estimation
Sequence kernel
Signal analysis
Speaker recognition
SVM
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Summary: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2008.01.034