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Titlebook: Artificial Intelligence; First CCF Internatio Zhi-Hua Zhou,Qiang Yang,Yu Zheng Conference proceedings 2018 Springer Nature Singapore Pte Lt

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發(fā)表于 2025-3-21 17:21:31 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Artificial Intelligence
期刊簡(jiǎn)稱First CCF Internatio
影響因子2023Zhi-Hua Zhou,Qiang Yang,Yu Zheng
視頻videohttp://file.papertrans.cn/163/162070/162070.mp4
學(xué)科分類Communications in Computer and Information Science
圖書(shū)封面Titlebook: Artificial Intelligence; First CCF Internatio Zhi-Hua Zhou,Qiang Yang,Yu Zheng Conference proceedings 2018 Springer Nature Singapore Pte Lt
影響因子This book constitutes the refereed proceedings of the First?CCF International Conference on Artificial Intelligence, CCF-ICAI 2018, held in Jinan, China in August, 2018.?The 17 papers presented were carefully reviewed and selected from 82 submissions. The papers are organized in topical sections on unsupervised learning,?graph-based and semi-supervised learning,?neural networks and deep learning,?planning and optimization,?AI applications..
Pindex Conference proceedings 2018
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書(shū)目名稱Artificial Intelligence影響因子(影響力)




書(shū)目名稱Artificial Intelligence影響因子(影響力)學(xué)科排名




書(shū)目名稱Artificial Intelligence網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Artificial Intelligence網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Artificial Intelligence被引頻次




書(shū)目名稱Artificial Intelligence被引頻次學(xué)科排名




書(shū)目名稱Artificial Intelligence年度引用




書(shū)目名稱Artificial Intelligence年度引用學(xué)科排名




書(shū)目名稱Artificial Intelligence讀者反饋




書(shū)目名稱Artificial Intelligence讀者反饋學(xué)科排名




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Learning Safe Graph Construction from?Multiple Graphsion which, however, remains challenging for general cases. What is more serious, constructing graph improperly may even deteriorate performance, which means its performance is worse than that of its supervised counterpart with only labeled data. For this reason, we consider learning a safe graph con
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Influence Maximization Node Mining with Trust Propagation Mechanismation mechanism of network information and controlling rumor. In recent years, based on the percolation theory, the problem of maximizing the node identification has attracted a lot of attention. However, this method does not consider the influence of the propagation of trust on the maximization of
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Semi-supervised Classification of Concept Drift Data Stream Based on Local Component Replacementing. These challenges will become more serious when only few instances are labeled in data stream. In the paper, based on the algorithm of SPASC, a strategy of local component replacement for updating classifier pool is proposed. The proposed strategy defines a vector based on local accuracy to eval
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RBF Networks with Dynamic Barycenter Averaging Kernel for Time Series Classification on function approximation. However, the core of RBF network is its static kernel function, which is based on the Euclidean distance and cannot be directly used for time series classification (TSC). In this paper, a new temporal kernel called Dynamic Barycenter Averaging Kernel (DBAK) is introduced
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