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Titlebook: New Frontiers in Mining Complex Patterns; 8th International Wo Michelangelo Ceci,Corrado Loglisci,Zbigniew Ras Conference proceedings 2020

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发表于 2025-3-21 20:09:21 | 显示全部楼层 |阅读模式
书目名称New Frontiers in Mining Complex Patterns
副标题8th International Wo
编辑Michelangelo Ceci,Corrado Loglisci,Zbigniew Ras
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: New Frontiers in Mining Complex Patterns; 8th International Wo Michelangelo Ceci,Corrado Loglisci,Zbigniew Ras Conference proceedings 2020
描述.This book constitutes the refereed post-conference proceedings of the 8th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2019, held in conjunction with ECML-PKDD 2019 in Würzburg, Germany, in September 2019.. The workshop focused on the latest developments in the analysis of complex and massive data sources, such as blogs, event or log data, medical data, spatio-temporal data, social networks, mobility data, sensor data and streams..
出版日期Conference proceedings 2020
关键词data mining; machine learning; multi-task learning; supervised learning; data stream mining; clustering; m
版次1
doihttps://doi.org/10.1007/978-3-030-48861-1
isbn_softcover978-3-030-48860-4
isbn_ebook978-3-030-48861-1Series 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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Disentangling Aspect and Opinion Words in Sentiment Analysis Using Lifelong PU Learning more challenging due to the lack of sufficient word-level aspect and opinion labels. To address it, we formulate the task in a Positive-Unlabeled (PU) learning setting and incorporate the idea of lifelong learning, which achieves promising results.
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0302-9743 s, NFMCP 2019, held in conjunction with ECML-PKDD 2019 in Würzburg, Germany, in September 2019.. The workshop focused on the latest developments in the analysis of complex and massive data sources, such as blogs, event or log data, medical data, spatio-temporal data, social networks, mobility data,
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Interpretable Survival Gradient Boosting Models with Bagged Trees Base Learnersr method produces competitive results often having the predictive power higher than full-complexity models. This is achieved while maintaining full interpretability of the model, which makes our method useful in medical applications.
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Len Feremans,Vincent Vercruyssen,Wannes Meert,Boris Cule,Bart Goethals
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