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Cawley, G. C. 
ORCID: https://orcid.org/0000-0002-4118-9095 and Talbot, N. L. C.
(2004)
Sparse Bayesian kernel logistic regression.
In: European Symposium on Artificial Neural Networks, 2004-04-28 - 2004-04-30.
Abstract
In this paper we present a simple hierarchical Bayesian treatment of the sparse kernel logistic regression (KLR) model based MacKay’s evidence approximation. The model is re-parameterised such that an isotropic Gaussian prior over parameters in the kernel induced feature space is replaced by an isotropic Gaussian prior over the transformed parameters, facilitating a Bayesian analysis using standard methods. The Bayesian approach allows the selection of “good” values for the usual regularisation and kernel parameters through maximisation of the marginal likelihood. Results obtained on a variety of benchmark datasets are provided indicating that the Bayesian kernel logistic regression model is competitive, whilst having one less parameter to determine during model selection.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Machine learning in computational biology (former - to 2018) Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences Faculty of Science > Research Groups > Statistics |
| Depositing User: | Vishal Gautam |
| Date Deposited: | 14 Jun 2011 19:11 |
| Last Modified: | 18 Jun 2026 21:07 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/22154 |
| DOI: |
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