Efficient approximate leave-one-out cross-validation for kernel logistic regression

Cawley, GC and Talbot, NLC (2008) Efficient approximate leave-one-out cross-validation for kernel logistic regression. Machine Learning, 71 (2-3). pp. 243-264.

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Abstract

Kernel logistic regression (KLR) is the kernel learning method best suited to binary pattern recognition problems where estimates of a-posteriori probability of class membership are required. Such problems occur frequently in practical applications, for instance because the operational prior class probabilities or equivalently the relative misclassification costs are variable or unknown at the time of training the model. The model parameters are given by the solution of a convex optimization problem, which may be found via an efficient iteratively re-weighted least squares (IRWLS) procedure. The generalization properties of a kernel logistic regression machine are however governed by a small number of hyper-parameters, the values of which must be determined during the process of model selection. In this paper, we propose a novel model selection strategy for KLR, based on a computationally efficient closed-form approximation of the leave-one-out cross-validation procedure. Results obtained on a variety of synthetic and real-world benchmark datasets are given, demonstrating that the proposed model selection procedure is competitive with a more conventional k-fold cross-validation based approach and also with Gaussian process (GP) classifiers implemented using the Laplace approximation and via the Expectation Propagation (EP) algorithm.

Item Type: Article
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: EPrints Services
Date Deposited: 01 Oct 2010 14:42
Last Modified: 25 Jul 2018 05:15
URI: https://ueaeprints.uea.ac.uk/id/eprint/3600
DOI: 10.1007/s10994-008-5055-9

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