Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple kernel ...

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 ...

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 ...

Multiple Kernel Learning (MKL) can learn an appropriate kernel combination from multiple base kernels for classification problems.

Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple ...

Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple kernel.

Sep 17, 2020 · It aims to learn “deep” kernel machines by exploring a combination of multiple kernels in a multi-layer structure. With multi-layer mapping, the ...

Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple kernel.