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Titlebook: Knowledge Science, Engineering and Management; 14th International C Han Qiu,Cheng Zhang,Sun-Yuan Kung Conference proceedings 2021 Springer

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發(fā)表于 2025-3-21 19:50:04 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Knowledge Science, Engineering and Management
副標(biāo)題14th International C
編輯Han Qiu,Cheng Zhang,Sun-Yuan Kung
視頻videohttp://file.papertrans.cn/545/544064/544064.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Knowledge Science, Engineering and Management; 14th International C Han Qiu,Cheng Zhang,Sun-Yuan Kung Conference proceedings 2021 Springer
描述This three-volume set constitutes the refereed proceedings of the 14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021, held in Tokyo, Japan, in August 2021..The 164 revised full papers were carefully reviewed and selected from 492 submissions. The contributions are organized in the following topical sections: knowledge science with learning and AI; knowledge engineering research and applications; knowledge management with optimization and security..
出版日期Conference proceedings 2021
關(guān)鍵詞artificial intelligence; computer networks; computer science; computer security; computer systems; comput
版次1
doihttps://doi.org/10.1007/978-3-030-82153-1
isbn_softcover978-3-030-82152-4
isbn_ebook978-3-030-82153-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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Beyond Laplacian Smoothing for?Semi-supervised Community Detectionraw features. In addition, we design a scheme to jointly optimize low-frequency and high-frequency information extraction on statistical modeling of Markov Random Fields. Extensive experiments demonstrate that the proposed CCDF model can consistently outperform or match state-of-the-art baselines in terms of semi-supervised community detection.
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An Overview of Methods for Acquiring and Generating Decision Modelsodel and Notation (DMN) standard, research interest has also increased regarding automatically extracting decision models. This paper discusses an overview and classification of such techniques, including generating decision models from various knowledge sources such as natural language text, legacy code, other models, or event logs.
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