Efficient model selection for kernel logistic regression

Cawley, G. C. and Talbot, N. L. C. (2004) Efficient model selection for kernel logistic regression. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR-2004), 2004-08-23 - 2004-08-26.

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Kernel logistic regression models, like their linear counterparts, can be trained using the efficient iteratively reweighted least-squares (IRWLS) algorithm. This approach suggests an approximate leave-one-out cross-validation estimator based on an existing method for exact leave-one-out cross-validation of least-squares models. Results compiled over seven benchmark datasets are presented for kernel logistic regression with model selection procedures based on both conventional k-fold and approximate leave-one-out cross-validation criteria, demonstrating the proposed approach to be viable.

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 19:08
Last Modified: 22 Apr 2020 09:12
URI: https://ueaeprints.uea.ac.uk/id/eprint/21602
DOI: 10.1109/ICPR.2004.1334249

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