Nested cross-validation (outer folds) model performance estimates for each multi-kernel learning method.

Uniform weights correspond to the sum of kernel matrices. For global weights, a wrapper method is used to learn the optimal weighted sum of kernel matrices. Hierarchical decomposition learns both localized weights and an optimal weighted sum of subset kernel matrices. The highest mean metric for each method is shown in bold.