Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Similarity Measures for Object-Oriented Case Representations
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
ICCBR '95 Proceedings of the First 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
Optimizing similarity assessment in case-based reasoning
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Dimensions of Case-Based Reasoner Quality Management
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: 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
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A very recent topic in CBR research deals with the automated optimisation of similarity measures—a core component of each CBR application—by using machine learning techniques. In our previous work, a number of approaches to bias and guide the learning process have been proposed aiming at more stable learning results and less susceptibility to overfitting. Those methods support the learner by incorporating background knowledge into the optimisation process. In this paper, we focus on one specific form of knowledge, namely vocabulary knowledge implicitly contained in the model of the respective application domain, as a source to enhance the learning of similarity measures.