%0 Journal Article
%T Sparsity-accuracy trade-off in MKL
%A Ryota Tomioka
%A Taiji Suzuki
%J Statistics
%D 2010
%I arXiv
%X 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.
%U http://arxiv.org/abs/1001.2615v1