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Titlebook: Database Systems for Advanced Applications; 24th International C Guoliang Li,Jun Yang,Yongxin Tong Conference proceedings 2019 Springer Nat

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發(fā)表于 2025-3-21 17:06:33 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Database Systems for Advanced Applications
副標題24th International C
編輯Guoliang Li,Jun Yang,Yongxin Tong
視頻videohttp://file.papertrans.cn/264/263431/263431.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Database Systems for Advanced Applications; 24th International C Guoliang Li,Jun Yang,Yongxin Tong Conference proceedings 2019 Springer Nat
描述.This two-volume set LNCS 11446 and LNCS 11447 constitutes the refereed proceedings of the 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, held in Chiang Mai, Thailand, in April 2019...The 92 full papers and 64 short papers were carefully selected from a total of 501 submissions. In addition, 13 demo papers and 6 tutorial papers are included. The full papers are organized in the following topics: big data; clustering and classification; crowdsourcing; data integration; embedding; graphs; knowledge graph; machine learning; privacy and graph; recommendation; social network; spatial; and spatio-temporal. The short papers, demo papers, and tutorial papers can be found in the volume LNCS 11448, which also includes the workshops of DASFAA 2019..
出版日期Conference proceedings 2019
關鍵詞artificial intelligence; data mining; computational linguistics; computer networks; data stream; image pr
版次1
doihttps://doi.org/10.1007/978-3-030-18579-4
isbn_softcover978-3-030-18578-7
isbn_ebook978-3-030-18579-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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An Exploration of Cross-Modal Retrieval for Unseen Concepts of the existing models are trained and tested in a close-set circumstance, which may easily fail on the newly emerged concepts that are never present in the training stage. In this paper, we propose a novel cross-modal hashing model, named Cross-Modal Attribute Hashing (CMAH), which can handle cros
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Continuous Patient-Centric Sequence Generation via Sequentially Coupled Adversarial Learningnt of these data is limited and the access to EHRs is difficult due to the issue of patient privacy. Thus high quality synthetic EHRs data is necessary to alleviate these issues. In this paper, we propose a Sequentially Coupled Generative Adversarial Network (SC-GAN) to generate continuous patient-c
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Reinforcement Learning to Diversify Top-N RecommendationTraditional approaches are to treat the process of constructing the recommendation list as a problem of greedy sequential items selection, which are inevitably sub-optimal. In this paper, we propose a reinforcement learning and neural networks based framework?– .ersify top-N Recommendation with .ast
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