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Titlebook: ECML PKDD 2020 Workshops; Workshops of the Eur Irena Koprinska,Michael Kamp,Jon Atle Gulla Conference proceedings 2020 Springer Nature Swit

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書目名稱ECML PKDD 2020 Workshops
副標(biāo)題Workshops of the Eur
編輯Irena Koprinska,Michael Kamp,Jon Atle Gulla
視頻videohttp://file.papertrans.cn/301/300279/300279.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: ECML PKDD 2020 Workshops; Workshops of the Eur Irena Koprinska,Michael Kamp,Jon Atle Gulla Conference proceedings 2020 Springer Nature Swit
描述This volume constitutes the refereed proceedings of the workshops which complemented the 20th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2020. Due to the COVID-19 pandemic the conference and workshops were held online.?.The 43 papers presented in volume were carefully reviewed and selected from numerous submissions.?The volume presents the papers that have been accepted for the following workshops: 5th Workshop on Data Science for Social Good, SoGood 2020; Workshop on Parallel, Distributed and Federated Learning, PDFL 2020;?Second Workshop on Machine Learning for Cybersecurity, MLCS 2020, 9th?International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2020,?Workshop on Data Integration and Applications, DINA 2020, Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning,?EDML 2020,?Second? International Workshop? on? eXplainable? Knowledge? Discovery in? Data Mining, XKDD 2020; 8th?International Workshop on News Recommendation and Analytics, INRA 2020.?.The papers from INRA 2020 are published open access and licensed under the terms of the Creative Commons Attributio
出版日期Conference proceedings 2020
關(guān)鍵詞artificial intelligence; computer hardware; computer networks; computer security; computer systems; corre
版次1
doihttps://doi.org/10.1007/978-3-030-65965-3
isbn_softcover978-3-030-65964-6
isbn_ebook978-3-030-65965-3Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Richard Beals,Roderick S. C. Wongrency and trust can be ensured in the underlying distributions and relationships of the resulting synthetic datasets. What is more, these datasets offer a strong level of privacy through lower risks of identifying real patients.
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Emergent Imputative Symbols: In One Worde staging information). In this paper, we explore the combination of pseudo time and topological data analysis to build realistic trajectories over disease topologies. Using three different datasets: simulated, diabetes and genomic data, we explore how the combined method can highlight distinct temp
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y unsupervised, our method outperforms many existing approaches. To the best of our knowledge, the only approaches with comparable performance require manual filtering of connections and feature engineering steps. In contrast, our method is applied to raw network traffic. We apply our pipeline to th
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