AverageMKL
AverageMKL is a simple wrapper defining the combination as the average of base kernels. Even if the average is a trivial solution, it is known to be a hard baseline in MKL. This wrapper helps the experimentation and the evaluation of this baseline against other complex approaches.
The kernels combination is trivially defined as
k_{\mu}(x,z)=\sum_r^P\mu_rk_r(x,z),\quad \mu_r=\frac{1}{P}
MKLpy.algorithms.AverageMKL(
learner=sklearn.svm.SVC(C=1000),
**kwargs,
)
| Parameter | Type | Description |
|---|---|---|
| learner | Object | the base learner dealing with the combined kernel |
| **kwargs | args | MKL parameters, see here |
| Attribute | Type | Description |
|---|---|---|
| n_kernels | int | number of combined kernels |
| KL | list | the training kernels list |
| func_form | callable | the combination function (e.g. summation, average...) |
| solution | dict | the solution of the optimization |
Methods
See standard MKL methods here
Examples
from MKLpy.algorithms import AverageMKL
mkl = AverageMKL()
mkl = mkl.fit(KLtr, Ytr)