Efficient Model Selection for Kernel Logistic Regression

Authors:
Gavin C. Cawley;Nicola L. C. Talbot
Affiliations:
University of East Anglia, Norwich, United Kingdom;University of East Anglia, Norwich, United Kingdom
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Year:
2004

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Abstract

Kernel logistic regression models, like their linear counterparts, can be trained using the efficient iteratively re-weighted 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.