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Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Frank Hutter,Kristian Kersting,Isabel Valera Conference proceed

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发表于 2025-3-21 17:09:10 | 显示全部楼层 |阅读模式
书目名称Machine Learning and Knowledge Discovery in Databases
副标题European Conference,
编辑Frank Hutter,Kristian Kersting,Isabel Valera
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Frank Hutter,Kristian Kersting,Isabel Valera Conference proceed
描述The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic..The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. ..The volumes are organized in topical sections as follows:..Part I:. Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion...Part II:. deep learning optimization and theory;active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning...Part III: .C
出版日期Conference proceedings 2021
关键词artificial intelligence; clustering algorithms; computer vision; correlation analysis; data mining; datab
版次1
doihttps://doi.org/10.1007/978-3-030-67658-2
isbn_softcover978-3-030-67657-5
isbn_ebook978-3-030-67658-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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https://doi.org/10.1007/978-3-030-67658-2artificial intelligence; clustering algorithms; computer vision; correlation analysis; data mining; datab
发表于 2025-3-22 12:04:55 | 显示全部楼层
Gauss Shift: Density Attractor Clustering Faster Than Mean Shift shift – a method that has linear time complexity. We quantify the characteristics of Gauss shift using synthetic datasets with known topologies. We further qualify Gauss shift using real-life data from active neuroscience research, which is the most comprehensive description of any subcellular organelle to date.. ..
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Maximum Margin Separations in Finite Closure Systemsclassification of finite subsets of the Euclidean space, we considered also the problem of vertex classification in graphs. Our experimental results provide clear evidence that maximal closed set separation with maximum margin results in a much better predictive performance than that with arbitrary maximal closed sets.
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A Relaxation-Based Approach for Mining Diverse Closed Patternsctive pruning, with an efficient branching rule, boosting the whole search process. We show experimentally that our approach significantly reduces the number of patterns and is very efficient in terms of running times, particularly on dense data sets.
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OMBA: User-Guided Product Representations for Online Market Basket Analysisble yet effective online method to generate products’ associations using their representations. Our extensive experiments on three real-world datasets show that OMBA outperforms state-of-the-art methods by as much as 21%, while emphasizing rarely occurring strong associations and effectively capturing temporal changes in associations.
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0302-9743 wledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic..The 232 full papers and 10 demo papers presented in this volume were carefully r
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