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Regularization Strategies and Empirical Bayesian Learning for MKL
This video was recorded at NIPS Workshops, Whistler 2010. Multiple kernel learning (MKL) has received considerable attention recently. In this paper, we show how different MKL algorithms can be understood as applications of different types of regularization on the kernel weights. Within the regularization view we consider in this paper, the Tikhonov-regularization-based formulation of MKL allows us to consider a generative probabilistic model behind MKL. Based on this model, we propose learning algorithms for the kernel weights through the maximization of marginalized likelihood.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
