State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow's Intelligent Network Traffic Control Systems

IEEE Communications Surveys and Tutorials

Volumn 19, Issue 4, 2017, Pages 2432-2455

  Fadlullah, Zubair Md ^a   Tang, Fengxiao ^a   Mao, Bomin ^a   Kato, Nei ^a   Akashi, Osamu ^b   Inoue, Takeru ^b   Mizutani, Kimihiro ^b  

^a TOHOKU UNIVERSITY   (Japan)

^b NTT Network Innovation Laboratories   (Japan)

Author keywords

artificial neural network; deep belief system; deep learning; machine intelligence; Machine learning; network traffic control; routing

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER CONTROL SYSTEMS; CONTROL SYSTEMS; FORTH (PROGRAMMING LANGUAGE); INTELLIGENT NETWORKS; LEARNING SYSTEMS; NEURAL NETWORKS; PACKET SWITCHING; SPEECH RECOGNITION;

BELIEF SYSTEMS; COMMUNICATION TECHNOLOGIES; CONVENTIONAL ROUTING; LEARNING ARCHITECTURES; MACHINE INTELLIGENCE; NETWORK TRAFFIC CONTROL; PACKET SWITCHED SYSTEMS; ROUTING;

DEEP LEARNING;

EID: 85032434210     PISSN: None     EISSN: 1553877X     Source Type: Journal    
DOI: 10.1109/COMST.2017.2707140     Document Type: Article

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