A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Neural networks and the bias/variance dilemma
Neural Computation
Machine Learning
Matrix computations (3rd ed.)
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Kernel Principal Component Analysis
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Convex Optimization
Efficient Model Selection for Kernel Logistic Regression
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Predictive Approaches for Choosing Hyperparameters in Gaussian Processes
Neural Computation
Gaussian Processes for Classification: Mean-Field Algorithms
Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Training a Support Vector Machine in the Primal
Neural Computation
Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters
The Journal of Machine Learning Research
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Sparse bayesian kernel survival analysis for modeling the growth domain of microbial pathogens
IEEE Transactions on Neural Networks
Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters
The Journal of Machine Learning Research
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
The Journal of Machine Learning Research
Revised mutual information approach for german text sentiment classification
Proceedings of the 22nd international conference on World Wide Web companion
Model selection in kernel ridge regression
Computational Statistics & Data Analysis
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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 optimization problem, which may be found via an efficient iteratively re-weighted least squares (IRWLS) procedure. The generalization properties of a kernel logistic regression machine are however governed by a small number of hyper-parameters, the values of which must be determined during the process of model selection. In this paper, we propose a novel model selection strategy for KLR, based on a computationally efficient closed-form approximation of the leave-one-out cross-validation procedure. Results obtained on a variety of synthetic and real-world benchmark datasets are given, demonstrating that the proposed model selection procedure is competitive with a more conventional k-fold cross-validation based approach and also with Gaussian process (GP) classifiers implemented using the Laplace approximation and via the Expectation Propagation (EP) algorithm.