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Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Albert Bifet,Jesse Davis,Indr? ?liobait? Confer

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發(fā)表于 2025-3-21 18:57:21 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track
副標(biāo)題European Conference,
編輯Albert Bifet,Jesse Davis,Indr? ?liobait?
視頻videohttp://file.papertrans.cn/621/620540/620540.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Albert Bifet,Jesse Davis,Indr? ?liobait? Confer
描述.This multi-volume set, LNAI 14941 to LNAI 14950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2024, held in Vilnius, Lithuania, in September 2024...?..The papers presented in these proceedings are from the following three conference tracks: -..Research Track:.?The 202 full papers presented here, from this track, were carefully reviewed and selected from 826 submissions. These papers are present in the following volumes: Part I, II, III, IV, V, VI, VII, VIII...?..Demo Track: .The 14 papers presented here, from this track, were selected from 30 submissions.?These papers are present in the following volume: Part VIII...?..Applied Data Science Track:?.The 56 full papers presented here, from this track, were carefully reviewed and selected from 224 submissions. These papers are present in the following volumes: Part IX and Part X..
出版日期Conference proceedings 2024
關(guān)鍵詞artificial intelligence; computer hardware; computer networks; computer security; computer systems; compu
版次1
doihttps://doi.org/10.1007/978-3-031-70365-2
isbn_softcover978-3-031-70364-5
isbn_ebook978-3-031-70365-2Series 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
The information of publication is updating

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Paolo Bonetti,Alberto Maria Metelli,Marcello Restellirojects and their effectiveness, including the differential impact based on type of aid. This volume is the first of its kind to unpack aid as a complex rather than a unitary concept and explore the wide areas 978-3-030-22123-2978-3-030-22121-8
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Towards Few-Shot Self-explaining Graph Neural Networks the . mimics the decision-making process, which makes predictions based on the generated explanation. Moreover, with a novel meta-training process and a designed mechanism that exploits task information, MSE-GNN can achieve remarkable performance on new few-shot tasks. Extensive experimental result
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