Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1609/aimag.v32i1.2331 | AI MAGAZINE |
Field | DocType | Volume |
Inductive transfer,Reuse,Computer science,Simulation,Knowledge transfer,Artificial intelligence software,Transfer of learning,Hyper-heuristic,Exploit,Artificial intelligence,Case-based reasoning,Machine learning | Journal | 32 |
Issue | ISSN | Citations |
SP1 | 0738-4602 | 2 |
PageRank | References | Authors |
0.37 | 27 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew Klenk | 1 | 32 | 2.86 |
| David W. Aha | 2 | 4103 | 620.93 |
| Matthew Molineaux | 3 | 116 | 13.83 |