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Theoretical Computer Science and General Issues

Neural Information Processing

23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part III

Editors: Akira, H., Seiichi, O., Doya, K., Kazushi, I., Minho, L., Derong, L. (Eds.)

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eBook $84.99
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  • ISBN 978-3-319-46675-0
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Softcover $109.99
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  • ISBN 978-3-319-46674-3
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About this book

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.

Table of contents (70 chapters)

Table of contents (70 chapters)
  • Chaotic Feature Selection and Reconstruction in Time Series Prediction

    Pages 3-11

    Hussein, Shamina (et al.)

  • L1/2 Norm Regularized Echo State Network for Chaotic Time Series Prediction

    Pages 12-19

    Xu, Meiling (et al.)

  • SVD and Text Mining Integrated Approach to Measure Effects of Disasters on Japanese Economics

    Pages 20-29

    Yano, Yuriko (et al.)

  • Deep Belief Network Using Reinforcement Learning and Its Applications to Time Series Forecasting

    Pages 30-37

    Hirata, Takaomi (et al.)

  • Neuron-Network Level Problem Decomposition Method for Cooperative Coevolution of Recurrent Networks for Time Series Prediction

    Pages 38-48

    Nand, Ravneil (et al.)

Buy this book

eBook $84.99
price for USA in USD (gross)
  • ISBN 978-3-319-46675-0
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $109.99
price for USA in USD
  • ISBN 978-3-319-46674-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Neural Information Processing
Book Subtitle
23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part III
Editors
  • Hirose Akira
  • Ozawa Seiichi
  • Kenji Doya
  • Ikeda Kazushi
  • Lee Minho
  • Liu Derong
Series Title
Theoretical Computer Science and General Issues
Series Volume
9949
Copyright
2016
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG
eBook ISBN
978-3-319-46675-0
DOI
10.1007/978-3-319-46675-0
Softcover ISBN
978-3-319-46674-3
Edition Number
1
Number of Pages
XVIII, 651
Number of Illustrations
215 b/w illustrations
Topics