The Case for Case-Based Transfer Learning

Klenk, Matthew (Navy Center for Applied Research in Artificial Intelligence) | Aha, David W. (Navy Center for Applied Research in Artificial Intelligence) | Molineaux, Matt (Knexus Research Corporation)

AI Magazine 

Transfer learning occurs when, after gaining experience from learning how to solve source problems, the same learner exploits this experience to improve performance and/or learning on target problems. In transfer learning, the differences between the source and target problems characterize the transfer distance. CBR can support transfer learning methods in multiple ways. We illustrate how CBR and transfer learning interact and characterize three approaches for using CBR in transfer learning: (1) as a transfer learning method, (2) for problem learning, and (3) to transfer knowledge between sets of problems.