Bottom-Up Induction of Feature Terms
Machine Learning
Relational instance-based learning with lists and terms
Machine Learning - Special issue on inducive logic programming
Similarity Measures for Structured Representations
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
On the Importance of Similitude: An Entropy-Based Assessment
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Similarity Measures for Object-Oriented Case Representations
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Cases as terms: A feature term approach to the structured representation of cases
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Structural Similarity as Guidance in Case-Based Design
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Components for Case-Based Reasoning Systems
CCIA '02 Proceedings of the 5th Catalonian Conference on AI: Topics in Artificial Intelligence
Web access path prediction using fuzzy case based reasoning
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
| Hi-index | 0.00 |
Reasoning and learning from cases are based on the concept of similarity often estimated by a distance. This paper presents LAUD, a distance measure that can be used to estimate similarity among relational cases. This measure is adequate for domains where cases are best represented by relations among entities. An experimental evaluation of the accuracy of LAUD is presented for the task of classifying marine sponges.