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Titlebook: Neural Information Processing; 23rd International C Akira Hirose,Seiichi Ozawa,Derong Liu Conference proceedings 2016 Springer Internationa

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書目名稱Neural Information Processing
副標題23rd International C
編輯Akira Hirose,Seiichi Ozawa,Derong Liu
視頻videohttp://file.papertrans.cn/664/663602/663602.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Neural Information Processing; 23rd International C Akira Hirose,Seiichi Ozawa,Derong Liu Conference proceedings 2016 Springer Internationa
描述.The four volume set LNCS 9947, LNCS 9948, LNCS 9949, and LNCS 9950 constitutes the proceedings of the 23rd International Conference on Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data analysis; robotics and control; bio-inspired/energy efficient information processing; whole brain architecture; neurodynamics; bioinformatics; biomedical engineering; data mining and cybersecurity workshop; machine learning; neuromorphic hardware; sensory perception; pattern recognition; social networks; brain-machine interface; computer vision; time series analysis; data-driven approach for extracting latent features; topological and graph based clustering methods; computational intelligence; data mining; deep neural networks; computational and cognitive neurosciences; theory and algorithms..
出版日期Conference proceedings 2016
關(guān)鍵詞embedded systems; genetic algorithms; neural networks; pattern recognition; swarm intelligence; big data;
版次1
doihttps://doi.org/10.1007/978-3-319-46687-3
isbn_softcover978-3-319-46686-6
isbn_ebook978-3-319-46687-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2016
The information of publication is updating

書目名稱Neural Information Processing影響因子(影響力)




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Establishing Mechanism of Warning for River Dust Event Based on an Artificial Neural Networkng PM.(T?+?1) concentration has six input factors include PM., PM. and meteorological condition. The train and test R was 0.8392 and 0.7900. PM.(T) was the most important factor in predicting PM.(T?+?1) by sensitivity analysis. Finally, mechanism constraints were established for warning of high PM.(T?+?1) concentrations in river basins.
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Cloud Monitoring Data Challenges: A Systematic Review, virtualization technology, energy, availability and performance. The results of this review are expected to help researchers and practitioners to understand cloud computing data challenges and develop innovative techniques and strategies to deal with these challenges.
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Deep Q-Learning with Prioritized Samplingwe use prioritized sampling into DQN as an alternative. Our experimental results demonstrate that DQN with prioritized sampling achieves a better performance, in terms of both average score and learning rate on four Atari 2600 games.
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