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Cawley, G. C. 
ORCID: https://orcid.org/0000-0002-4118-9095 and Talbot, N. L. C.
(2004)
Efficient model selection for kernel logistic regression.
In: 17th International Conference on Pattern Recognition, 2004-08-23 - 2004-08-26.
Abstract
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 |
| UEA Research Groups: | Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Data Science and Statistics Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences |
| Depositing User: | Vishal Gautam |
| Date Deposited: | 14 Jun 2011 19:08 |
| Last Modified: | 22 Apr 2023 02:46 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/21602 |
| DOI: | 10.1109/ICPR.2004.1334249 |
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