COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Considering Decision Cost During Learning of Feature Weights
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Acquiring Customer Preferences from Return-Set Selections
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Learning Feature Weights from Case Order Feedback
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Improvements in K-Nearest Neighbor Classification
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
Learning a Local Similarity Metric for Case-Based Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
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
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Modeling Decisions: Information Fusion and Aggregation Operators (Cognitive Technologies)
Modeling Decisions: Information Fusion and Aggregation Operators (Cognitive Technologies)
Noise Tolerant Variants of the Perceptron Algorithm
The Journal of Machine Learning Research
Optimizing similarity assessment in case-based reasoning
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
Using evolution programs to learn local similarity measures
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
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
CBR Supports Decision Analysis with Uncertainty
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Maintaining Footprint-Based Retrieval for Case Deletion
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
CBTV: visualising case bases for similarity measure design and selection
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Similarity adaptation in an exploratory retrieval scenario
AMR'10 Proceedings of the 8th international conference on Adaptive Multimedia Retrieval: context, exploration, and fusion
An experimental comparison of similarity adaptation approaches
AMR'11 Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
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The performance of a case-based reasoning system often depends on the suitability of an underlying similarity (distance) measure, and specifying such a measure by hand can be very difficult. In this paper, we therefore develop a machine learning approach to similarity assessment. More precisely, we propose a method that learns how to combine given local similarity measures into a global one. As training information, the method merely assumes qualitative feedback in the form of similarity comparisons, revealing which of two candidate cases is more similar to a reference case. Experimental results, focusing on the ranking performance of this approach, are very promising and show that good models can be obtained with a reasonable amount of training information.