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.

• Type: Conference
• Year: 2004
• Where: ICPR
• Authors: Gavin C. Cawley, Nicola L. C. Talbot
• Keywords: Approximate Leave-one-out Cross-validation, Computer Vision, Exact Leave-one-out Cross-validation, ICPR 2004, Leave-one-out Cross-validation Estimator