Incorporation of radius-info can be simple with SimpleMKL
Recent research has shown the benefit of incorporating the radius of the Minimal Enclosing Ball (MEB) of training data into Multiple Kernel Learning (MKL). However, straightforwardly incorporating this radius leads to complex learning structure and considerably increased computation. Moreover, the notorious sensitivity of this radius to outliers can adversely affect MKL. In this paper, instead of directly incorporating the radius of MEB, we incorporate its close relative, the trace of data...[Show more]
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