Sparsity-accuracy trade-off in MKL

01/15/2010
by   Ryota Tomioka, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset