Artificial Intelligence Review - Special issue on lazy learning
Adaptation-guided retrieval: questioning the similarity assumption in reasoning
Artificial Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
An Adaptation Heuristic for Case-Based Estimation
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Learning to Improve Case Adaption by Introspective Reasoning and CBR
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Case-Based Reasoning Technology, From Foundations to Applications
Learning Adaptation Rules from a Case-Base
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Using case-base data to learn adaptation knowledge for design
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Introspective learning to build case-based reasoning (CBR) knowledge containers
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Informed case base maintenance: a complexity profiling approach
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Case-based reasoning in radiotherapy planning
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
| Hi-index | 0.00 |
Most CBR systems in operation today are ‘retrieval-only' in that they do not adapt the solutions of retrieved cases. Adaptation is, in general, a difficult problem that often requires the acquisition and maintenance of a large body of explicit domain knowledge. For certain machine-learning tasks, however, adaptation can be performed successfully using only knowledge contained within the case base itself. One such task is regression (i.e. predicting the value of a numeric variable). This paper presents a knowledge-light regression algorithm in which the knowledge required to solve a query is generated from the differences between pairs of stored cases. Experiments show that this technique performs well relative to standard algorithms on a range of datasets.