Neural Information Processing

1. Front Matter

   Pages I-XVIII

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2. Machine Learning

   1. Front Matter

      Pages 1-1

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   2. Improving Generalization Capability of Extreme Learning Machine with Synthetic Instances Generation

      • Wei Ao, Yulin He, Joshua Zhexue Huang, Yupeng He

      Pages 3-12

   3. Adaptive \(L_p\) \((0<p<1)\) Regularization: Oracle Property and Applications

      • Yunxiao Shi, Xiangnan He, Han Wu, Zhong-Xiao Jin, Wenlian Lu

      Pages 13-23

   4. Fuzzy Self-Organizing Incremental Neural Network for Fuzzy Clustering

      • Tianyue Zhang, Baile Xu, Furao Shen

      Pages 24-32

   5. Stochastic Online Kernel Selection with Instantaneous Loss in Random Feature Space

      • Zhizhuo Han, Shizhong Liao

      Pages 33-42

   6. Topology Learning Embedding: A Fast and Incremental Method for Manifold Learning

      • Tao Zhu, Furao Shen, Jinxi Zhao, Yu Liang

      Pages 43-52

   7. Hybrid RVM Algorithm Based on the Prediction Variance

      • Fang Liu, Fei Zhao, Mi Tong, Yan Yang, Zhenhao Yu

      Pages 53-63

   8. Quality Control for Crowdsourced Multi-label Classification Using RAkEL

      • Kosuke Yoshimura, Yukino Baba, Hisashi Kashima

      Pages 64-73

   9. A Self-adaptive Growing Method for Training Compact RBF Networks

      • Baile Xu, Furao Shen, Jinxi Zhao, Tianyue Zhang

      Pages 74-81

   10. Incremental Extreme Learning Machine via Fast Random Search Method

       • Zhihui Lao, Zhiheng Zhou, Junchu Huang

       Pages 82-90

   11. Learning of Phase-Amplitude-Type Complex-Valued Neural Networks with Application to Signal Coherence

       • Rongrong Wu, He Huang, Tingwen Huang

       Pages 91-99

   12. Application of Instruction-Based Behavior Explanation to a Reinforcement Learning Agent with Changing Policy

       • Yosuke Fukuchi, Masahiko Osawa, Hiroshi Yamakawa, Michita Imai

       Pages 100-108

   13. Using Flexible Neural Trees to Seed Backpropagation

       • Peng Wu, Jeff Orchard

       Pages 109-116

   14. Joint Neighborhood Subgraphs Link Prediction

       • Dinh Tran-Van, Alessandro Sperduti, Fabrizio Costa

       Pages 117-123

   15. Multimodal Fusion with Global and Local Features for Text Classification

       • Cheng Xu, Yue Wu, Zongtian Liu

       Pages 124-134

   16. Learning Deep Neural Network Based Kernel Functions for Small Sample Size Classification

       • Tieran Zheng, Jiqing Han, Guibin Zheng

       Pages 135-143

   17. Relation Classification via CNN, Segmented Max-pooling, and SDP-BLSTM

       • Pengfei Wang, Zhipeng Xie, Junfeng Hu

       Pages 144-154

   18. Binary Stochastic Representations for Large Multi-class Classification

       • Thomas Gerald, Nicolas Baskiotis, Ludovic Denoyer

       Pages 155-165

   19. Solving the Local-Minimum Problem in Training Deep Learning Machines

       • James Ting-Ho Lo, Yichuan Gui, Yun Peng

       Pages 166-174

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. 

• Adaptive dynamic programming
• Artificial intelligence
• Biologically inspired computing
• Brain-computer interface
• Computational intelligence
• Artificial Intelligence
• machine learning
• learning algorithms
• neural networks
• Support Vector Machines (SVM
• data mining
• signal processing
• reinforcement learning
• genetic algorithms
• clustering algorithms
• support vector
• classifiers
• algorithm analysis and problem complexity

• Guangdong University of Technology, Guangzhou, China

  Derong Liu, Shengli Xie

• South China University of Technology, Guangzhou, China

  Yuanqing Li

• Institute of Automation, Chinese Academy of Sciences, Beijing, China

  Dongbin Zhao

• King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

  El-Sayed M. El-Alfy

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