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  • Conference proceedings
  • © 2016

Neural Information Processing

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

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 9949)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Conference series link(s): ICONIP: International Conference on Neural Information Processing

Conference proceedings info: ICONIP 2016.

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Table of contents (70 papers)

  1. Front Matter

    Pages I-XVIII
  2. Time Series Analysis

    1. Front Matter

      Pages 1-1
    2. Chaotic Feature Selection and Reconstruction in Time Series Prediction

      • Shamina Hussein, Rohitash Chandra
      Pages 3-11
    3. L1/2 Norm Regularized Echo State Network for Chaotic Time Series Prediction

      • Meiling Xu, Min Han, Shunshoku Kanae
      Pages 12-19
    4. Deep Belief Network Using Reinforcement Learning and Its Applications to Time Series Forecasting

      • Takaomi Hirata, Takashi Kuremoto, Masanao Obayashi, Shingo Mabu, Kunikazu Kobayashi
      Pages 30-37
  3. Data-Driven Approach for Extracting Latent Features from Multi-dimensional Data

    1. Front Matter

      Pages 49-49
    2. Yet Another Schatten Norm for Tensor Recovery

      • Chao Li, Lili Guo, Yu Tao, Jinyu Wang, Lin Qi, Zheng Dou
      Pages 51-60
    3. Combining Deep Learning and Preference Learning for Object Tracking

      • Shuchao Pang, Juan José del Coz, Zhezhou Yu, Oscar Luaces, Jorge Díez
      Pages 70-77
    4. Nonnegative Tensor Train Decompositions for Multi-domain Feature Extraction and Clustering

      • Namgil Lee, Anh-Huy Phan, Fengyu Cong, Andrzej Cichocki
      Pages 87-95
    5. Features Learning and Transformation Based on Deep Autoencoders

      • Eric Janvier, Thierry Couronne, Nistor Grozavu
      Pages 111-118
    6. t-Distributed Stochastic Neighbor Embedding with Inhomogeneous Degrees of Freedom

      • Jun Kitazono, Nistor Grozavu, Nicoleta Rogovschi, Toshiaki Omori, Seiichi Ozawa
      Pages 119-128
  4. Topological and Graph Based Clustering Methods

    1. Front Matter

      Pages 129-129

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.

Editors and Affiliations

  • The University of Tokyo , Tokyo, Japan

    Akira Hirose

  • Kobe University , Kobe, Japan

    Seiichi Ozawa

  • Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  • Nara Institute of Science and Technology , Ikoma, Japan

    Kazushi Ikeda

  • Kyungpook National University , Daegu, Korea (Republic of)

    Minho Lee

  • Chinese Academy of Sciences , Beijing, China

    Derong Liu

Bibliographic Information

Buy it now

Buying options

eBook USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access