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Kernel learning at the first level of inference
2014
Neural Networks
Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However,
doi:10.1016/j.neunet.2014.01.011
pmid:24561452
fatcat:iszwkmrzabg57eksbfcmyruz7m