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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 17:06:45 | 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; classification methods; computer vision; data mining; graph theory; Human-Comput
版次1
doihttps://doi.org/10.1007/978-3-030-67664-3
isbn_softcover978-3-030-67663-6
isbn_ebook978-3-030-67664-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
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Towards Description of Block Model on Graph is intractable even for simple cases, e.g., when the underlying graph is a tree with just two blocks. However, simple and efficient ILP formulations and algorithms exist for its relaxation and yield insights different from a state-of-the-art related work in unsupervised description. We empirically
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