Seonglae ChoMultiple Kernel Learning (MKL) has been proposed as an approach to simultaneously learn the kernel weights and the associated decision function in supervised learning settings.
In MKL the kernel can be considered as a linear combination of basis kernels
The solution of the learning problem for kernel methods can be written as
In MKL we learn both coefficients and in a single optimization problem.