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Titlebook: Cognitive Computing – ICCC 2018; Second International Jing Xiao,Zhi-Hong Mao,Liang-Jie Zhang Conference proceedings 2018 The Editor(s) (if

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21#
發(fā)表于 2025-3-25 06:36:48 | 只看該作者
22#
發(fā)表于 2025-3-25 10:58:42 | 只看該作者
0302-9743 aspects of Sensing Intelligence (SI) as a Service (SIaaS). Cognitive Computing is a sensing-driven computing (SDC) schema that explores and integrates intelligence from all types of senses in various scenarios and solution contexts..978-3-319-94306-0978-3-319-94307-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
23#
發(fā)表于 2025-3-25 12:46:31 | 只看該作者
The Photon-Photon Correlation Methodmbedding. LINE and PUHE are incorporated to learn the user social network connection embedding. The proposed method is evaluated on SMP CUP 2017 user profiling competition dataset. The experiment results demonstrate that leveraging both user behavior embedding and social network connection embedding improves the user profiling efficiently.
24#
發(fā)表于 2025-3-25 17:20:23 | 只看該作者
On the Bases of a Free Lie Algebra present a cognitive-based system for social network analysis. Our system supports information discovery of interesting social patterns from big uncertain social networks—which are represented in the form of key-value pairs—capturing the perceived likelihood of the linkages among the social entities in the network.
25#
發(fā)表于 2025-3-25 22:13:21 | 只看該作者
26#
發(fā)表于 2025-3-26 03:10:05 | 只看該作者
27#
發(fā)表于 2025-3-26 04:17:43 | 只看該作者
Conference proceedings 2018A, in June 2018.?The 15 papers presented in this volume were carefully reviewed and selected from numerous submissions. The papers cover all aspects of Sensing Intelligence (SI) as a Service (SIaaS). Cognitive Computing is a sensing-driven computing (SDC) schema that explores and integrates intellig
28#
發(fā)表于 2025-3-26 12:21:05 | 只看該作者
Selected Topics on Electron Physicsstruct a feature system applied to opinion mining. The experimental results on CCF BDCI 2017 aspect-based sentiment analysis shared task dataset show that our proposed pair-wise method obtained good performance with a 0.718 F1 score which outperforms most proposed methods.
29#
發(fā)表于 2025-3-26 13:15:20 | 只看該作者
https://doi.org/10.1007/978-0-8176-8256-9ions. In this paper, we apply the new algorithm to build a deep neural network (DNN) learning system in our Texas Holdem poker game program. The contrast poker program has gained third rank in Annual Computer Poker Competition 2017 (ACPC 2017) and system with new approach shows better performance while convergence much faster.
30#
發(fā)表于 2025-3-26 19:46:14 | 只看該作者
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