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Titlebook: Discovery Science; 24th International C Carlos Soares,Luis Torgo Conference proceedings 2021 Springer Nature Switzerland AG 2021 applied co

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樓主
發(fā)表于 2025-3-21 17:29:41 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Discovery Science
副標(biāo)題24th International C
編輯Carlos Soares,Luis Torgo
視頻videohttp://file.papertrans.cn/282/281064/281064.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Discovery Science; 24th International C Carlos Soares,Luis Torgo Conference proceedings 2021 Springer Nature Switzerland AG 2021 applied co
描述This book constitutes the proceedings of the 24th International Conference on Discovery Science, DS 2021, which took place virtually during October 11-13, 2021..The 36 papers presented in this volume were carefully reviewed and selected from 76 submissions. The contributions were organized in topical sections named: applications; classification; data streams; graph and network mining; machine learning for COVID-19; neural networks and deep learning; preferences and recommender systems; representation learning and feature selection; responsible artificial intelligence; and spatial, temporal and spatiotemporal data...?.
出版日期Conference proceedings 2021
關(guān)鍵詞applied computing; artificial intelligence; batch learning; classification and regression trees; compute
版次1
doihttps://doi.org/10.1007/978-3-030-88942-5
isbn_softcover978-3-030-88941-8
isbn_ebook978-3-030-88942-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

書目名稱Discovery Science影響因子(影響力)




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書目名稱Discovery Science網(wǎng)絡(luò)公開度學(xué)科排名




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發(fā)表于 2025-3-21 21:19:22 | 只看該作者
https://doi.org/10.1007/978-3-030-88942-5applied computing; artificial intelligence; batch learning; classification and regression trees; compute
板凳
發(fā)表于 2025-3-22 00:30:42 | 只看該作者
978-3-030-88941-8Springer Nature Switzerland AG 2021
地板
發(fā)表于 2025-3-22 05:52:49 | 只看該作者
https://doi.org/10.1007/978-3-658-04158-8iased ground-truth of the graders. In this paper, we focus on the automated grading of free-text responses. We formulate the problem as a binary classification problem of two class labels: low- and high-grade. We present a benchmark on four machine learning methods using three experiment protocols o
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發(fā)表于 2025-3-22 11:18:08 | 只看該作者
Willensfreiheit, Physik und Hirnforschungare based on heuristics which rely on a set of software metrics and corresponding threshold values. Those techniques and tools suffer from subjectivity issues, discordant results among the tools, and the reliability of the thresholds. To mitigate these problems, we used machine learning to automate
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發(fā)表于 2025-3-22 15:33:46 | 只看該作者
https://doi.org/10.1007/978-3-658-04158-8ntegrate multiple HTML tables into a single table for retrieval of information containing in various Web pages. The method is designed by extending tree-structured LSTM, the neural network for tree-structured data, in order to extract information that is both linguistic and structural information of
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發(fā)表于 2025-3-23 04:21:27 | 只看該作者
Willensfreiheit, Physik und Hirnforschungnce introduces new challenges for both the performance assessment of these models and their predictive modeling. While several performance metrics have been established as baselines in balanced domains, some cannot be applied to the imbalanced case since the use of the majority class in the metric c
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發(fā)表于 2025-3-23 06:19:12 | 只看該作者
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