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Titlebook: Neural Information Processing; 24th International C Derong Liu,Shengli Xie,El-Sayed M. El-Alfy Conference proceedings 2017 Springer Interna

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書(shū)目名稱(chēng)Neural Information Processing
副標(biāo)題24th International C
編輯Derong Liu,Shengli Xie,El-Sayed M. El-Alfy
視頻videohttp://file.papertrans.cn/664/663570/663570.mp4
概述Includes supplementary material:
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Neural Information Processing; 24th International C Derong Liu,Shengli Xie,El-Sayed M. El-Alfy Conference proceedings 2017 Springer Interna
描述The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017. The 563 ?full papers presented were carefully reviewed and selected from 856 submissions. The 6 volumes are organized in topical sections on?Machine Learning,?Reinforcement Learning, Big Data Analysis, Deep Learning, Brain-Computer Interface, Computational Finance, Computer Vision, Neurodynamics, Sensory Perception and Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series Analysis, Social Networks, Bioinformatics, Information Security and Social Cognition, Robotics and Control, Pattern Recognition, Neuromorphic Hardware and Speech Processing.?.
出版日期Conference proceedings 2017
關(guān)鍵詞Adaptive dynamic programming; Artificial intelligence; Biologically inspired computing; Brain-computer
版次1
doihttps://doi.org/10.1007/978-3-319-70090-8
isbn_softcover978-3-319-70089-2
isbn_ebook978-3-319-70090-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
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n this paper is that the complexity of future railway systems of systems can be addressed with advantage only by a higher degree of distribution of functions on local interoperable computers - communicating by means of standard protocols - and by adopting a multi-level formal modelling suitable to s
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Huazheng Zhou,Xiaohu Ma,Lina Bianhe new network from scratch—effectively “wasting” its earlier work on verifying the smaller network. In this paper, we present an enhancement to abstraction-based verification of neural networks, by using .: the process of utilizing information acquired when verifying an abstract network, in order t
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