Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple kernel ...
Our optimization problem subsumes state-of-the-art approaches to multiple kernel learning, covering sparse and non- sparse MKL by arbitrary p-norm ...
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 ...
Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple ...
In this paper, we propose an Lp norm multiple kernel learning algorithm in the primal where we resort to the alternating optimization method: one cycle for ...
In this paper, we present a group-based local adaptive deep multiple kernel learning (GLDMKL) method with lp norm.
The combination parameters can also be restricted using extra constraints, such as the ℓp- norm on the kernel weights or trace restriction on the combined ...
Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple ...
In this contribution, we study the limits and benefits of classical, sparse MKL and the recent `p MKL [5], which outputs non-sparse kernel combinations, in ...
Multiple Kernel Learning (MKL) can learn an appropriate kernel combination from multiple base kernels for classification problems.