Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
408865 | Neurocomputing | 2008 | 8 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
J.D.B. Nelson, R.I. Damper, S.R. Gunn, B. Guo,