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Register a new userSparsity-accuracy trade-off in MKL
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Added by
Ryota Tomioka
Publication date
2010
fields
Mathematical Statistics
and research's language is
English
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We empirically investigate the best trade-off between sparse and uniformly-weighted multiple kernel learning (MKL) using the elastic-net regularization on real and simulated datasets. We find that the best trade-off parameter depends not only on the sparsity of the true kernel-weight spectrum but also on the linear dependence among kernels and the number of samples.
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