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Bayesian Efficient Multiple Kernel Learning
[article]
2012
arXiv
pre-print
Multiple kernel learning algorithms are proposed to combine kernels in order to obtain a better similarity measure or to integrate feature representations coming from different data sources. Most of the previous research on such methods is focused on the computational efficiency issue. However, it is still not feasible to combine many kernels using existing Bayesian approaches due to their high time complexity. We propose a fully conjugate Bayesian formulation and derive a deterministic
arXiv:1206.6465v1
fatcat:2jqr2524czh4vaiotl62rdm7pi