Efficient approximate leave-one-out cross-validation for kernel logistic regression

DOI10.1007/S10994-008-5055-9zbMath1470.62113OpenAlexW1986328771MaRDI QIDQ1009261

Gavin C. Cawley, Nicola L. C. Talbot

Publication date: 31 March 2009

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-008-5055-9

zbMATH Keywords

model selectionkernel logistic regression

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Generalized linear models (logistic models) (62J12) Pattern recognition, speech recognition (68T10)

Related Items (4)

Model selection in kernel ridge regression ⋮ Extreme logistic regression ⋮ Enhanced kriging leave-one-out cross-validation in improving model estimation and optimization ⋮ Kernel learning at the first level of inference

Uses Software

• LAPACK
• CLAPACK
• alr3

Cites Work

• Fast exact leave-one-out cross-validation of sparse least-squares support vector machines
• Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers.
• Weighted least squares support vector machines: robustness and sparse approximation
• Support-vector networks
• Some results on Tchebycheffian spline functions and stochastic processes
• Predictive Approaches for Choosing Hyperparameters in Gaussian Processes
• LAPACK Users' Guide
• The Relationship between Variable Selection and Data Agumentation and a Method for Prediction
• Training a Support Vector Machine in the Primal
• Bayesian Kernel Learning Methods for Parametric Accelerated Life Survival Analysis
• Feature Scaling for Kernel Fisher Discriminant Analysis Using Leave-One-Out Cross Validation
• A Simplex Method for Function Minimization
• Choosing multiple parameters for support vector machines
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