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Xingguo Chen
Researcher at Nanjing University of Posts and Telecommunications
Publications - 23
Citations - 570
Xingguo Chen is an academic researcher from Nanjing University of Posts and Telecommunications. The author has contributed to research in topics: Reinforcement learning & Computer science. The author has an hindex of 8, co-authored 20 publications receiving 302 citations. Previous affiliations of Xingguo Chen include Beijing Technology and Business University & Nanjing University.
Papers
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Journal ArticleDOI
Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data.
Kangyang Chen,Hexia Chen,Chuanlong Zhou,Yichao Huang,Xiangyang Qi,Ruqin Shen,Fengrui Liu,Min Zuo,Xinyi Zou,Jinfeng Wang,Yan Zhang,Da Chen,Xingguo Chen,Xingguo Chen,Yongfeng Deng,Yongfeng Deng,Hongqiang Ren +16 more
TL;DR: Compared to other 7 models, decision tree (DT), random forest (RF) and deep cascade forest (DCF) trained by data sets of pH, DO, CODMn, and NH3-N had significantly better performance in prediction of all 6 Levels of water quality recommended by Chinese government.
Journal ArticleDOI
Multi-Agent Game Abstraction via Graph Attention Neural Network
TL;DR: A novel game abstraction mechanism based on two-stage attention network (G2ANet) is proposed, which can indicate whether there is an interaction between two agents and the importance of the interaction and two novel learning algorithms GA-Comm and GA-AC are proposed.
Proceedings Article
A Survey of Point-of-Interest Recommendation in Location-Based Social Networks
Yonghong Yu,Xingguo Chen +1 more
TL;DR: A brief survey over the task of Point-of-Interest recommendation in LBSNs is presented and some research directions for Point- of- interest recommendation are discussed, including how to integrate additional information into POI recommendation algorithms.
Journal ArticleDOI
Online Selective Kernel-Based Temporal Difference Learning
Xingguo Chen,Yang Gao,Ruili Wang +2 more
TL;DR: An online selective kernel-based temporal difference (OSKTD) learning algorithm is proposed to deal with large scale and/or continuous reinforcement learning problems and can reach a competitive ultimate optima compared with the up-to-date algorithms.
Journal ArticleDOI
RL-DOT: A Reinforcement Learning NPC Team for Playing Domination Games
Hao Wang,Yang Gao,Xingguo Chen +2 more
TL;DR: In this paper, the design of reinforcement-learning-based domination team (RL-DOT), a nonplayer character (NPC) team for playing Unreal Tournament (UT) Domination games is described and a Q- learning-style algorithm is used to learn the optimal decision-making policy.