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Efficient approximate leave-one-out cross-validation for kernel logistic regression
2008
Machine Learning
Kernel logistic regression (KLR) is the kernel learning method best suited to binary pattern recognition problems where estimates of a-posteriori probability of class membership are required. Such problems occur frequently in practical applications, for instance because the operational prior class probabilities or equivalently the relative misclassification costs are variable or unknown at the time of training the model. The model parameters are given by the solution of a convex optimisation
doi:10.1007/s10994-008-5055-9
fatcat:lwyqgrujrrbarhbn7p6ud3xdiq