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Titlebook: Case-Based Reasoning Research and Development; 32nd International C Juan A. Recio-Garcia,Mauricio G. Orozco-del-Castil Conference proceedin

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发表于 2025-3-21 19:36:36 | 显示全部楼层 |阅读模式
书目名称Case-Based Reasoning Research and Development
副标题32nd International C
编辑Juan A. Recio-Garcia,Mauricio G. Orozco-del-Castil
视频videohttp://file.papertrans.cn/243/242036/242036.mp4
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Case-Based Reasoning Research and Development; 32nd International C Juan A. Recio-Garcia,Mauricio G. Orozco-del-Castil Conference proceedin
描述.This book constitutes the refereed proceedings of the 32nd International Conference on Case-Based Reasoning Research and Development, ICCBR 2024, held in Merida, Mexico, during July 1–4, 2024...The 29 full papers included in this book were carefully reviewed and selected from 91 submissions. They cover a wide range of CBR topics of interest both to practitioners and researchers, including: improvements to the CBR methodology itself: case representation, similarity, retrieval, adaptation, etc.; synergies with other Artificial Intelligence topics, such as Explainable AI and Large Language Models; and finally a whole catalog of applications to different domains such as health-care, education, and legislation..
出版日期Conference proceedings 2024
关键词Computer Science; Informatics; Conference Proceedings; Research; Applications; Artificial Intelligence; Ca
版次1
doihttps://doi.org/10.1007/978-3-031-63646-2
isbn_softcover978-3-031-63645-5
isbn_ebook978-3-031-63646-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Nida Farheen,Subarna Chatterjeerows while maintaining performance. The performance is compared to two baselines: never updating and always updating. Our experiments with public datasets show that a smart updating strategy catches the drifting of case base content and similarity measures well.
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https://doi.org/10.1007/978-3-030-52440-1on rules and rule engines for complex adaptations in POCBR in this paper. The results of an experimental evaluation indicate that the rule-based adaptation approach leads to significantly better results during runtime than an already available POCBR adaptation method.
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,Automatic Adjusting Global Similarity Measures in Learning CBR Systems,rows while maintaining performance. The performance is compared to two baselines: never updating and always updating. Our experiments with public datasets show that a smart updating strategy catches the drifting of case base content and similarity measures well.
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Improving Complex Adaptations in Process-Oriented Case-Based Reasoning by Applying Rule-Based Adapton rules and rule engines for complex adaptations in POCBR in this paper. The results of an experimental evaluation indicate that the rule-based adaptation approach leads to significantly better results during runtime than an already available POCBR adaptation method.
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