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ICPR
2004
IEEE

Efficient Model Selection for Kernel Logistic Regression

9 years 6 months ago
Efficient Model Selection for Kernel Logistic Regression
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.
Gavin C. Cawley, Nicola L. C. Talbot
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2004
Where ICPR
Authors Gavin C. Cawley, Nicola L. C. Talbot
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