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Titlebook: Discovery Science; 25th International C Poncelet Pascal,Dino Ienco Conference proceedings 2022 The Editor(s) (if applicable) and The Author

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樓主
發(fā)表于 2025-3-21 18:19:09 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Discovery Science
副標題25th International C
編輯Poncelet Pascal,Dino Ienco
視頻videohttp://file.papertrans.cn/282/281053/281053.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Discovery Science; 25th International C Poncelet Pascal,Dino Ienco Conference proceedings 2022 The Editor(s) (if applicable) and The Author
描述.This book constitutes the proceedings of the 25th International Conference on Discovery Science, DS 2022, which took place virtually during October 10-12, 2022...The 27 full papers and 12 short papers presented in this volume were carefully reviewed and selected from 59 submissions. ?.
出版日期Conference proceedings 2022
關鍵詞artificial intelligence; clustering algorithms; computer networks; computer systems; computer vision; cor
版次1
doihttps://doi.org/10.1007/978-3-031-18840-4
isbn_softcover978-3-031-18839-8
isbn_ebook978-3-031-18840-4Series 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
The information of publication is updating

書目名稱Discovery Science影響因子(影響力)




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書目名稱Discovery Science網(wǎng)絡公開度學科排名




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沙發(fā)
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Vergleichende Au?en- und Sicherheitspolitikstructures to summarize the information contained in the training examples, which can be quickly updated and allows to retrieve the best rule for each incoming example. The behavior of i. is evaluated with different parameterizations, and compared to other best-known incremental symbolic learning algorithms such as . and ..
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Studienerfolg und Studienabbruchnd analyze two strategies for conducting policy evaluation under cumulative periodic rewards, and study them by making use of simulation environments. Our findings indicate that both strategies can achieve similar sample efficiency as when we have consistent rewards.
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Policy Evaluation with?Delayed, Aggregated Anonymous Feedbacknd analyze two strategies for conducting policy evaluation under cumulative periodic rewards, and study them by making use of simulation environments. Our findings indicate that both strategies can achieve similar sample efficiency as when we have consistent rewards.
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