Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Using Introspective Learning to Improve Retrieval in CBR: A Case Study in Air Traffic Control
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Defining Similarity Measures: Top-Down vs. Bottom-Up
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Using evolution programs to learn local similarity measures
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
On the use of vocabulary knowledge for learning similarity measures
WM'05 Proceedings of the Third Biennial conference on Professional Knowledge Management
Learning similarity measures: a formal view based on a generalized CBR model
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Learning Similarity Functions from Qualitative Feedback
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Affordance-based similarity measurement for entity types
COSIT'07 Proceedings of the 8th international conference on Spatial information theory
Engineering Applications of Artificial Intelligence
Semantic spatial web services with case-based reasoning
W2GIS'06 Proceedings of the 6th international conference on Web and Wireless Geographical Information Systems
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The definition of accurate similarity measures is a key issue of every Case-Based Reasoning application. Although some approaches to optimize similarity measures automatically have already been applied, these approaches are not suited for all CBR application domains. On the one hand, they are restricted to classification tasks. On the other hand, they only allow optimization of feature weights. We propose a novel learning approach which addresses both problems, i.e. it is suited for most CBR application domains beyond simple classification and it enables learning of more sophisticated similarity measures.