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Titlebook: Case-Based Reasoning Research and Development; 8th International Co Lorraine McGinty,David C. Wilson Conference proceedings 2009 Springer-V

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樓主: concord
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發(fā)表于 2025-3-23 11:22:40 | 只看該作者
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發(fā)表于 2025-3-23 15:12:20 | 只看該作者
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發(fā)表于 2025-3-23 18:02:02 | 只看該作者
People Search Engines and Public Records,arnt by a genetic algorithm. The system has been applied to a breast cancer application domain. The results show that with our methodology we can improve the results obtained with a case base in which attributes have been manually selected by physicians, saving physicians work in future.
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發(fā)表于 2025-3-23 22:27:13 | 只看該作者
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發(fā)表于 2025-3-24 04:12:04 | 只看該作者
https://doi.org/10.1007/978-1-4842-2820-3nner. An algorithm that incorporates CBR techniques into the Heuristically Accelerated Q–Learning is also proposed. Empirical evaluations were conducted in a simulator for the RoboCup Four-Legged Soccer Competition, and results obtained shows that using CB-HARL, the agents learn faster than using either RL or HARL methods.
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發(fā)表于 2025-3-24 09:06:31 | 只看該作者
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發(fā)表于 2025-3-24 13:51:34 | 只看該作者
Rosendo Abellera,Lakshman Bulusuexecutions for meta-reasoning. We illustrate its benefits with experimental results from a system implementing our approach called . in a real-time strategy game. The evaluation of Meta-Darmok shows that the system successfully adapts itself and its performance improves through appropriate revision of the case base.
18#
發(fā)表于 2025-3-24 18:41:38 | 只看該作者
https://doi.org/10.1007/978-1-4842-3264-4 the defined measures are applicable to any representation language for which a refinement lattice can be defined. We empirically evaluate our measures comparing them to other measures in the literature in a variety of relational data sets showing very good results.
19#
發(fā)表于 2025-3-24 22:20:34 | 只看該作者
20#
發(fā)表于 2025-3-25 00:10:30 | 只看該作者
Improving Reinforcement Learning by Using Case Based Heuristicsnner. An algorithm that incorporates CBR techniques into the Heuristically Accelerated Q–Learning is also proposed. Empirical evaluations were conducted in a simulator for the RoboCup Four-Legged Soccer Competition, and results obtained shows that using CB-HARL, the agents learn faster than using either RL or HARL methods.
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