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

Enlightening

書目名稱Discovery Science影響因子(影響力)




書目名稱Discovery Science影響因子(影響力)學科排名




書目名稱Discovery Science網絡公開度




書目名稱Discovery Science網絡公開度學科排名




書目名稱Discovery Science被引頻次




書目名稱Discovery Science被引頻次學科排名




書目名稱Discovery Science年度引用




書目名稱Discovery Science年度引用學科排名




書目名稱Discovery Science讀者反饋




書目名稱Discovery Science讀者反饋學科排名

波動

978-3-030-61526-0Springer Nature Switzerland AG 2020

Intercept

Discovery Science978-3-030-61527-7Series ISSN 0302-9743 Series E-ISSN 1611-3349

colony

發(fā)源

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

痛苦一下

痛苦一下

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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