Preliminary Analysis of Multiple Kernel Learning: Flat Maxima, Diversity and Fisher Information
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چکیده
Gaining an insight into the potential effectiveness of Multiple Kernel Learning (MKL) methods requires analysis of the underlying ensemble process and resulting loss. An attempt to suggest such directions is described in this work which presents some preliminary results based on the “Flat Maximum Effect” and decompositions of the loss which leads to MKL problem being recast as one of optimal Design of Experiments (DoE).
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تاریخ انتشار 2009