Sparse Bayesian kernel logistic regression

G. C. Cawley, N. L. C. Talbot

Research output: Contribution to conferencePaper


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.
Original languageEnglish
Number of pages6
Publication statusPublished - Apr 2004
EventEuropean Symposium on Artificial Neural Networks - Bruges, Belgium
Duration: 28 Apr 200430 Apr 2004


ConferenceEuropean Symposium on Artificial Neural Networks
Abbreviated titleESANN-2004

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