To allow for robust kernel mixtures, we generalize MKL to arbitrary Lp-norms. We devise new insights on the connection between several existing MKL formulations ...
Missing: $ell_p | Show results with:$ell_p
We presented an efficient and accurate approach to non-sparse multiple kernel learning and showed that our `p-norm MKL can be motivated as Tikhonov and ...
Missing: $ell_p | Show results with:$ell_p
To allow for robust kernel mixtures, we generalize MKL to arbitrary 'p-norms. We devise new insights on the connection between several existing MKL formulations ...
Missing: $ell_p | Show results with:$ell_p
We presented an efficient and accurate approach to non-sparse multiple kernel learning and showed that our `p-norm MKL can be motivated as Tikhonov and ...
Missing: $ell_p | Show results with:$ell_p
To allow for robust kernel mixtures, we generalize MKL to arbitrary ℓp-norms. We devise new insights on the connection between several existing MKL formulations ...
Missing: $ell_p | Show results with:$ell_p
Aug 19, 2020 · We present a multiple kernel learning algorithm where a general \ell_p-norm constraint (p\geq1) on kernel weights is considered.
This work devise new insights on the connection between several existing MKL formulations and develop two efficient interleaved optimization strategies for ...
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The paper addresses the multiple kernel learning (MKL) problem for one-class classification (OCC). For this purpose, based on the Fisher null-space ...
Jun 1, 2014 · Our objective is to develop formulations and algorithms for efficiently computing the feature selection path -- i.e. the variation in ...
The paper poses the one-class MKL task as a min-max saddle point Lagrangian optimisation problem and proposes an efficient alternating optimisation method ...