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Titlebook: Knowledge Management and Acquisition for Intelligent Systems; 16th Pacific Rim Kno Kouzou Ohara,Quan Bai Conference proceedings 2019 Spring

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書目名稱Knowledge Management and Acquisition for Intelligent Systems
副標題16th Pacific Rim Kno
編輯Kouzou Ohara,Quan Bai
視頻videohttp://file.papertrans.cn/544/543968/543968.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Knowledge Management and Acquisition for Intelligent Systems; 16th Pacific Rim Kno Kouzou Ohara,Quan Bai Conference proceedings 2019 Spring
描述This book constitutes the proceedings of the 16th International Workshop on Knowledge Management and Acquisition for Intelligent Systems, PKAW 2019, held in Cuvu, Fiji, in August 2019..The 9 full papers and 7 short papers included in this volume were carefully reviewed and selected from 38 initial submissions. The papers cover advanced research work that contributes to the technical and theoretical aspects in the ?elds of intelligent systems/agents, natural language processing, and applications of machine learning techniques including Deep Learning to real world problems..
出版日期Conference proceedings 2019
關鍵詞artificial intelligence; clustering; computer architecture; computer networks; data mining; natural langu
版次1
doihttps://doi.org/10.1007/978-3-030-30639-7
isbn_softcover978-3-030-30638-0
isbn_ebook978-3-030-30639-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
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Toxicity Prediction by Multimodal Deep Learning,mance that could go beyond individual performance of each data representation or each neural network type. On a standard toxicity benchmark, our proposed method obtains significantly better accuracy levels than that by the state-of-the-art toxicity prediction methods.
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Context-Aware Influence Diffusion in Online Social Networks,nce propagation patterns under different scenarios. The results show that context-aware influence diffusion turns out to be an experienced model, where beliefs formed through users’ past experiences affect the adoption of influences.
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Network Embedding via Link Strength Adjusted Random Walk,pture the structural information. Further more, the strengths of links are updated using the embedding output as feedback. Through experiments we have verified that our method out performs state-of-the-art network embedding methods, in node classification tasks and link prediction tasks.
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Estimating Difficulty Score of Visual Search in Images for Semi-supervised Object Detection,human annotators in PASCAL VOC2012. Eventually, we demostrate with experiments that our method has an ability of selecting suitable samples to improve the performance of detectors in a semi-supervised task.
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