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2004

Sparse Bayesian kernel logistic regression

8 years 11 months ago
Sparse Bayesian kernel logistic regression
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.
Gavin C. Cawley, Nicola L. C. Talbot
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where ESANN
Authors Gavin C. Cawley, Nicola L. C. Talbot
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