Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple models that are competitive on a ...

Transfer learning speeds up model training by reusing pre-trained models for new tasks. It reduces data needs, enhancing performance and progress in new ML applications. Transfer learning is limited ...