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Titlebook: Discovery Science; 23rd International C Annalisa Appice,Grigorios Tsoumakas,Stan Matwin Conference proceedings 2020 Springer Nature Switzer

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书目名称Discovery Science
副标题23rd International C
编辑Annalisa Appice,Grigorios Tsoumakas,Stan Matwin
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Discovery Science; 23rd International C Annalisa Appice,Grigorios Tsoumakas,Stan Matwin Conference proceedings 2020 Springer Nature Switzer
描述.This book constitutes the proceedings of the 23rd International Conference on Discovery Science, DS 2020, which took place during October 19-21, 2020. The conference was planned to take place in Thessaloniki, Greece, but had to change to an online format due to the COVID-19 pandemic. ..The 26 full and 19 short papers presented in this volume were carefully reviewed and selected from 76 submissions. The contributions were organized in topical sections named: classification; clustering; data and knowledge representation; data streams; distributed processing; ensembles; explainable and interpretable machine learning; graph and network mining; multi-target models; neural networks and deep learning; and spatial, temporal and spatiotemporal data..
出版日期Conference proceedings 2020
关键词artificial intelligence; classification; clustering; computer networks; data communication systems; data
版次1
doihttps://doi.org/10.1007/978-3-030-61527-7
isbn_softcover978-3-030-61526-0
isbn_ebook978-3-030-61527-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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978-3-030-61526-0Springer Nature Switzerland AG 2020
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Discovery Science978-3-030-61527-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Studienpionier:innen und Soziale Arbeitrtinent issue. This line of investigation is particularly lacking within clinical decision-making, for which the consequences can be life-altering. Certain real-world clinical ML decision tools are known to demonstrate significant levels of discrimination. There is currently indication that fairness
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https://doi.org/10.1007/978-3-7091-6825-7ting work is algorithm-specific, limited to simple together and apart constraints and does not attempt to satisfy all constraints. This limits applications including where satisfying all constraints is required such as fairness. In this work, we take the novel direction of post-processing the result
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Bernd-Christian Funk,Bernd Schilcherbe extremely hard. On the other hand, applying traditional clustering and feature reduction techniques to the highly dimensional raw pixel space can be ineffective. To overcome these problems, we propose to use a deep convolutional embedding clustering framework. The model simultaneously optimizes t
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