Signal Theory for SVM Kernel Parameter Estimation

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 th...

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Bibliographic Details
Main Authors: Nelson, J.D.B., Damper, R.I., Gunn, S.R., Guo, B.
Format: Conference Proceeding
Language:English
Subjects:
Computer science
Gunn devices
Hilbert space
Kernel
Machine learning
Parameter estimation
Sampling methods
Space technology
Support vector machine classification
Support vector machines
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Description
Summary: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 sine 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.
ISSN:1551-2541
2378-928X
DOI:10.1109/MLSP.2006.275539