Titlebook: Web Information Systems and Applications; 16th International C Weiwei Ni,Xin Wang,Yukun Li Conference proceedings 2019 Springer Nature Swit

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書(shū)目名稱Web Information Systems and Applications影響因子(影響力)




書(shū)目名稱Web Information Systems and Applications影響因子(影響力)學(xué)科排名




書(shū)目名稱Web Information Systems and Applications網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Web Information Systems and Applications網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Web Information Systems and Applications被引頻次




書(shū)目名稱Web Information Systems and Applications被引頻次學(xué)科排名




書(shū)目名稱Web Information Systems and Applications年度引用




書(shū)目名稱Web Information Systems and Applications年度引用學(xué)科排名




書(shū)目名稱Web Information Systems and Applications讀者反饋




書(shū)目名稱Web Information Systems and Applications讀者反饋學(xué)科排名

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Intelligent Trader Model Based on Deep Reinforcement Learnings a game process under incomplete information, and the single-objective supervised learning model is difficult to deal with such serialization decision problems. Reinforcement learning is one of the effective ways to solve these problems. This paper proposes an ISTG model (Intelligent Stock Trader a

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Intelligent Trader Model Based on Deep Reinforcement Learnings a game process under incomplete information, and the single-objective supervised learning model is difficult to deal with such serialization decision problems. Reinforcement learning is one of the effective ways to solve these problems. This paper proposes an ISTG model (Intelligent Stock Trader a

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Semi-supervised Learning to Rank with Uncertain Data retrieval, the pseudo labels created by semi-supervised learning may not reliable. The uncertain data nearby the boundaries of relevant and irrelevant documents for a given query has a significant impact on the effectiveness of learning to rank. Therefore, how to utilize the uncertain data to bring

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Semi-supervised Learning to Rank with Uncertain Data retrieval, the pseudo labels created by semi-supervised learning may not reliable. The uncertain data nearby the boundaries of relevant and irrelevant documents for a given query has a significant impact on the effectiveness of learning to rank. Therefore, how to utilize the uncertain data to bring

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Signed Network Embedding Based on Noise Contrastive Estimation and Deep Learning the network structure. Signed networks are a kind of networks with both positive and negative edges, which have been widely used in real life. Presently, the mainstream signed network embedding algorithms mainly focus on the difference between positive and negative edges, but ignore the role of emp