GP-based kernel evolution for L2-Regularization Networks

Abstract

In kernel-based learning methods, a crucial design parameter is given by the choice of the kernel function to be used. Although there is, in theory, an infinite range of potential candidates, a handful of kernels covers the majority of actual applications. Partly, this is due to the difficulty of choosing an optimal kernel function in absence of a-priori… (More)
DOI: 10.1109/CEC.2014.6900389
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