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Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part IV: Lecture Notes in Computer Science, cartea 9950

Editat de Akira Hirose, Seiichi Ozawa, Kenji Doya, Kazushi Ikeda, Minho Lee, Derong Liu
en Limba Engleză Paperback – 30 sep 2016
The four volume set LNCS 9947, LNCS 9948, LNCS 9949, and LNCS 9950 constitues 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.
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Specificații

ISBN-13: 9783319466804
ISBN-10: 3319466801
Pagini: 682
Ilustrații: XIX, 663 p. 254 illus.
Dimensiuni: 155 x 235 x 35 mm
Greutate: 0.94 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Theoretical Computer Science and General Issues

Locul publicării:Cham, Switzerland

Caracteristici

Includes supplementary material: sn.pub/extras

Cuprins

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.