Machine learning of inductive bias
Machine learning of inductive bias
Explanation-based learning: a survey of programs and perspectives
ACM Computing Surveys (CSUR)
Models of incremental concept formation
Artificial Intelligence
Knowledge representation in a case-based reasoning system: defaults
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Reasoning with Incomplete Information
Reasoning with Incomplete Information
Classification in the KL-ONE knowledge representation system
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
On the comparison of theories: preferring the most specific explanation
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Hi-index | 0.00 |
This paper is concerned with knowledge representation issues in machine learning. In particular, it presents a representation language that supports a hybrid analytical and similarity-based classification scheme. Analytical classification is produced using a KL-ONE-like term-subsumption strategy, while similarity-based classification is driven by generalizations induced from a training set by an unsupervised learning procedure. This approach can be seen as providing an inductive bias to the learning procedure, thereby shortening the required training phase, and reducing the brittleness of the induced generalizations.