Titlebook: Case-Based Reasoning; 18th International C Isabelle Bichindaritz,Stefania Montani Conference proceedings 2010 Springer-Verlag Berlin Heidel

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https://doi.org/10.1007/978-1-4899-0585-7ms on predictions for many learning algorithms including case-base reasoning (CBR). When learning under concept drift, a critical issue is to identify and determine “when” and “how” the concept changes. In this paper, we developed a . between case chunks and then proposed a change detection method b

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Operational Research and Systemsin parallel with techniques such as cross-validation accuracy comparison. In this paper we present the Case Base Topology Viewer (CBTV) which allows the application of different similarity measures to a case base to be visualised so that system designers can explore the case base and the associated

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Introduction to Oracle Global Data Services,ttices from binary and complex data. These concept lattices provide a realistic basis for knowledge base organization and ontology engineering. More generally, they can be used for representing knowledge and reasoning in knowledge systems and CBR systems as well.

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