Sparse Bayesian kernel logistic regression

Research output: Contribution to conferencePaper

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.
Original languageEnglish
Pages133-138
Number of pages6
Publication statusPublished - Apr 2004
EventProceedings of the European Symposium on Artificial Neural Networks (ESANN-2004) - Bruges, Belgium
Duration: 28 Apr 200430 Apr 2004

Conference

ConferenceProceedings of the European Symposium on Artificial Neural Networks (ESANN-2004)
Country/TerritoryBelgium
CityBruges
Period28/04/0430/04/04

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