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Multiple kernel learning for multiple sources
This video was recorded at NIPS Workshop on Learning from Multiple Sources, Whistler 2008. In this talk, I will consider the problem of learning a predictor from multiple sources of information, a situation common in many domains such as computer vision or bioinformatics. I will focus primarily on the multiple kernel learning framework, which amounts to consider one positive definite kernel for each source of information. Natural unanswered questions arise in this context, namely: Can one learn from infinitely many sources? Should one prefer closely related sources, or very different sources? Is it worth considering a large kernel-induced feature space as multiple sources?
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
