Nelson, J. D. B., Damper, R. I., Gunn, S. R. and Guo, B.
Signal theory for SVM kernel design with applications to parameter estimation and sequence kernels.
Neurocomputing, 72, (1-3), . (doi:10.1016/j.neucom.2008.01.034).
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
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