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====Optimization approaches====
These approaches solve an optimization problem to determine parameters for the kernel combination function. This has been done with similarity measures and structural risk minimization approaches. For similarity measures such as the one defined above, the problem can be formulated as follows<ref>Gert
Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research,
5:27–72, 2004a</ref>:
:<math>\max_{\beta,\operatorname{tr}(K')=1,K'\ge0} A(K',YY^T).</math>
[[Structural risk minimization]] approaches that have been used include linear approaches, such as that used by Lanckriet et al (2002)<ref>Gert R. G. Lanckriet, Nello Cristianini, Peter Bartlett, Laurent El Ghaoui, and Michael I. Jordan.
Learning the kernel matrix with semidefinite programming. In Proceedings of the 19th International
Conference on Machine Learning, 2002</ref>. We can define the implausibility of a kernel <math>\omega(K)</math> to be the value of the objective function after solving a canonical SVM problem. We can then maximizez as follows
====Bayesian approaches====
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