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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/10.1007/978-1-4899-0322-8d case base sizes do not cause retrieval efficiency to degrade to the extent that it becomes a problem. We also show how complicated case base maintenance solutions intended to address the utility problem can actually decrease the combined system efficiency.
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Case-Based Plan Diversity characteristic and support our claim, we define the metric of “plan diversity” and put it to test using plans for a real-time strategy game, a domain chosen for the simplicity and clarity of its tasks and the quantifiable results it generates.
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Reducing the Memory Footprint of Temporal Difference Learning over Finitely Many States by Using Casstic effects and might have long term repercussions on the outcome of the game. The main conclusion from our experiments is that by using case-based generalization, the size of the Q-table can be substantially reduced while maintaining the quality of the RL estimates.
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Visualization for the Masses: Learning from the Expertsoning techniques to help users visualize complex datasets. We exploit an online visualization service, ManyEyes, and explore how case-based representation of datasets including simple features such as size and content types can produce recommendations to assist novice users in the selection of appropriate visualization types.
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Detecting Change via Competence Modelinite domain through finite samples and requires no prior knowledge about the case distribution, which makes it more practical in real world applications. Also, different from many other change detection methods, we not only detect the change of concepts but also quantify and describe this change.
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