Sparse Bayesian kernel logistic regression

Cawley, G. C. and Talbot, N. L. C. (2004) Sparse Bayesian kernel logistic regression. In: Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2004), 2004-04-28 - 2004-04-30.

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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

University of East Anglia > Faculty of Science > Research Groups > Computational Biology (subgroups are shown below) > Machine learning in computational biology
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Depositing User: Vishal Gautam
Date Deposited: 14 Jun 2011 19:11
Last Modified: 23 Jul 2020 23:51
URI: https://ueaeprints.uea.ac.uk/id/eprint/22154
DOI:

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