Agnostic learning versus prior knowledge in the design of kernel machines

Cawley, Gavin C ORCID: and Talbot, Nicola L. C (2007) Agnostic learning versus prior knowledge in the design of kernel machines. In: IEEE/INNS International Joint Conference on Neural Networks, 2007-08-12 - 2007-08-17.

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The optimal model parameters of a kernel machine are typically given by the solution of a convex optimisation problem with a single global optimum. Obtaining the best possible performance is therefore largely a matter of the design of a good kernel for the problem at hand, exploiting any underlying structure and optimisation of the regularisation and kernel parameters, i.e. model selection. Fortunately, analytic bounds on, or approximations to, the leave-one-out cross-validation error are often available, providing an efficient and generally reliable means to guide model selection. However, the degree to which the incorporation of prior knowledge improves performance over that which can be obtained using "standard" kernels with automated model selection (i.e. agnostic learning), is an open question. In this paper, we compare approaches using example solutions for all of the benchmark tasks on both tracks of the IJCNN-2007 Agnostic Learning versus Prior Knowledge Challenge.

Item Type: Conference or Workshop Item (Paper)
Faculty \ School: Faculty of Science > School of Computing Sciences

UEA Research Groups: Faculty of Science > Research Groups > Data Science and Statistics
Faculty of Science > Research Groups > Computational Biology
Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences
Depositing User: Vishal Gautam
Date Deposited: 04 Apr 2011 13:25
Last Modified: 22 Apr 2023 02:44
DOI: 10.1109/IJCNN.2007.4371219

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