Efficient model selection for kernel logistic regression

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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.
Original languageEnglish
Number of pages4
Publication statusPublished - Aug 2004
EventProceedings of the 17th International Conference on Pattern Recognition (ICPR-2004) -
Duration: 23 Aug 200426 Aug 2004


ConferenceProceedings of the 17th International Conference on Pattern Recognition (ICPR-2004)

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