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Titlebook: Case-Based Reasoning Research and Development; 30th International C Mark T. Keane,Nirmalie Wiratunga Conference proceedings 2022 The Editor

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書目名稱Case-Based Reasoning Research and Development
副標(biāo)題30th International C
編輯Mark T. Keane,Nirmalie Wiratunga
視頻videohttp://file.papertrans.cn/223/222324/222324.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Case-Based Reasoning Research and Development; 30th International C Mark T. Keane,Nirmalie Wiratunga Conference proceedings 2022 The Editor
描述This book constitutes the proceedings of the 30th International Conference on Case-Based Reasoning, ICCBR 2022, which took place in Nancy, France, during September 12-15, 2022..The theme of ICCBR 2022 was Global Challenges for CBR aiming to consider how CBR can and might contribute to challenges in sustainability, climate change, and global health. The 26 papers presented in this volume were carefully reviewed and selected from 68 submissions. They deal with AI and related research focusing on comparison and integration of CBR with other AI methods such as deep learning architectures, reinforcement learning, lifelong learning, and eXplainable AI (XAI)..
出版日期Conference proceedings 2022
關(guān)鍵詞artificial intelligence; case base; case based reasoning; cbr; computer networks; computer science; comput
版次1
doihttps://doi.org/10.1007/978-3-031-14923-8
isbn_softcover978-3-031-14922-1
isbn_ebook978-3-031-14923-8Series 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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978-3-031-14922-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/222324.jpg
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A Note on the Wave Climatology of UK Waters Machine Learning or black-box models. Existing XAI libraries offer a good number of explanation methods, that are reusable for different domains and models, with different choices of parameters. However, it is not clear what would be a good explainer for a given situation, domain, AI model, and use
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Alison Schwartz-Kripner,Cornelia P. ChanningHence, the popular methods generate synthetic counterfactuals using “blind” perturbation, by manipulating feature values to elicit a class change. However, this strategy has other problems, notably a tendency to generate invalid data points that are . or that involve feature-values that do not natur
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W. Mestwerdt,O. Müller,H. Brandauper we show how this can be addressed in a case-based reasoning (CBR) context by a metric learning strategy that explicitly considers bias/fairness. Since one of the advantages CBR has over alternative machine learning approaches is interpretability, it is interesting to see how much this metric lea
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Critical Fluctuations Under Shear Flow,erize domains. The effectiveness of machine learning with deep neural networks has prompted much interest in neural network approaches to feature learning in case-based reasoning, with several works showing the value of feature extraction from input data using convolutional neural networks. Those ap
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