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Kernel Functional Optimisation
conference contribution
posted on 2022-09-28, 06:09 authored by A V Arun Kumar, Alistair ShiltonAlistair Shilton, Santu RanaSantu Rana, Sunil GuptaSunil Gupta, Svetha VenkateshSvetha VenkateshTraditional methods for kernel selection rely on parametric kernel functions or a combination thereof and although the kernel hyperparameters are tuned, these methods often provide sub-optimal results due to the limitations induced by the parametric forms. In this paper, we propose a novel formulation for kernel selection using efficient Bayesian optimisation to find the best fitting non-parametric kernel. The kernel is expressed using a linear combination of functions sampled from a prior Gaussian Process (GP) defined by a hyperkernel. We also provide a mechanism to ensure the positive definiteness of the Gram matrix constructed using the resultant kernels. Our experimental results on GP regression and Support Vector Machine (SVM) classification tasks involving both synthetic functions and several real-world datasets show the superiority of our approach over the state-of-the-art.
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Volume
6Pagination
4725 - 4737ISSN
1049-5258ISBN-13
9781713845393Publication classification
E1 Full written paper - refereedTitle of proceedings
Advances in Neural Information Processing SystemsUsage metrics
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