Open AccessThe Case for Case‐Based Transfer Learning
Author(s) -
Klenk Matthew,
Aha David W.,
Molineaux Matt
Publication year - 2011
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v32i1.2331
Subject(s) - transfer of learning , inductive transfer , computer science , artificial intelligence , reuse , transfer problem , process (computing) , machine learning , transfer (computing) , case based reasoning , multi task learning , exploit , semi supervised learning , robot learning , engineering , task (project management) , operating system , computer security , parallel computing , international trade , robot , systems engineering , business , mobile robot , waste management
Case‐based reasoning (CBR) is a problem‐solving process in which a new problem is solved by retrieving a similar situation and reusing its solution. Transfer learning occurs when, after gaining experience from learning how to solve source problems, the same learner exploits this experience to improve performance and 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. We describe examples of these approaches from our own and related work and discuss applicable transfer distances for each. We close with conclusions and directions for future research applying CBR to transfer learning.