Neural Information Processing, Part I

Neural Information Processing: 24th International Conference, ICONIP 2017, Guangzhou, China, November 14-18, 2017, Proceedings, Part I (Lecture Notes in Computer Science)

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.

Table of Contents

Part 1 Machine Learning
Chapter 1. Improving Generalization Capability of Extreme Learning Machine with Synthetic Instances Generation
Chapter 2. Adaptive Lp (0<p<1) Regularization: Oracle Property and Applications
Chapter 3. Fuzzy Self-Organizing Incremental Neural Network for Fuzzy Clustering
Chapter 4. Stochastic Online Kernel Selection with Instantaneous Loss in Random Feature Space
Chapter 5. Topology Learning Embedding: A Fast and Incremental Method for Manifold Learning
Chapter 6. Hybrid RVM Algorithm Based on the Prediction Variance
Chapter 7. Quality Control for Crowdsourced Multi-label Classification Using RAkEL
Chapter 8. A Self-adaptive Growing Method for Training Compact RBF Networks
Chapter 9. Incremental Extreme Learning Machine via Fast Random Search Method
Chapter 10. Learning of Phase-Amplitude-Type Complex-Valued Neural Networks with Application to Signal Coherence
Chapter 11. Application of Instruction-Based Behavior Explanation to a Reinforcement Learning Agent with Changing Policy
Chapter 12. Using Flexible Neural Trees to Seed Backpropagation
Chapter 13. Joint Neighborhood Subgraphs Link Prediction
Chapter 14. Multimodal Fusion with Global and Local Features for Text Classification
Chapter 15. Learning Deep Neural Network Based Kernel Functions for Small Sample Size Classification
Chapter 16. Relation Classification via CNN, Segmented Max-pooling, and SDP-BLSTM
Chapter 17. Binary Stochastic Representations for Large Multi-class Classification
Chapter 18. Solving the Local-Minimum Problem in Training Deep Learning Machines
Chapter 19. The Sample Selection Model Based on Improved Autoencoder for the Online Questionnaire Investigation
Chapter 20. Hybrid Collaborative Recommendation via Semi-AutoEncoder
Chapter 21. Time Series Classification with Deep Neural Networks Based on Hurst Exponent Analysis
Chapter 22. Deep Learning Model for Sentiment Analysis in Multi-lingual Corpus
Chapter 23. Differential Evolution Memetic Document Clustering Using Chaotic Logistic Local Search
Chapter 24. Completion of High Order Tensor Data with Missing Entries via Tensor-Train Decomposition
Chapter 25. GASOM: Genetic Algorithm Assisted Architecture Learning in Self Organizing Maps
Chapter 26. A Nonnegative Projection Based Algorithm for Low-Rank Nonnegative Matrix Approximation
Chapter 27. Multi-view Label Space Dimension Reduction
Chapter 28. Large-Margin Supervised Hashing
Chapter 29. Three-Dimensional Surface Feature for Hyperspectral Imagery Classification
Chapter 30. Stochastic Sequential Minimal Optimization for Large-Scale Linear SVM
Chapter 31. Robust Kernel Approximation for Classification
Chapter 32. A Multiobjective Multiclass Support Vector Machine Restricting Classifier Candidates Based on k-Means Clustering
Chapter 33. Multi-label Learning with Label-Specific Feature Selection
Chapter 34. Neural Networks for Efficient Nonlinear Online Clustering
Chapter 35. Multiple Scale Canonical Correlation Analysis Networks for Two-View Object Recognition
Chapter 36. A Novel Newton-Type Algorithm for Nonnegative Matrix Factorization with Alpha-Divergence
Chapter 37. Iterative Local Hyperlinear Learning Based Relief for Feature Weight Estimation
Chapter 38. Projected Kernel Recursive Least Squares Algorithm
Chapter 39. Resource Allocation and Optimization Based on Queuing Theory and BP Network
Chapter 40. Linear Dimensionality Reduction for Time Series
Chapter 41. An Effective Martin Kernel for Time Series Classification
Chapter 42. Text Classification Using Lifelong Machine Learning
Chapter 43. Wake-Sleep Variational Autoencoders for Language Modeling
Chapter 44. Educational and Non-educational Text Classification Based on Deep Gaussian Processes
Chapter 45. A Generalized I-ELM Algorithm for Handling Node Noise in Single-Hidden Layer Feedforward Networks
Chapter 46. Locality-Sensitive Term Weighting for Short Text Clustering
Chapter 47. A Comparison of Supervised Machine Learning Algorithms for Classification of Communications Network Traffic
Chapter 48. Emotion Classification from Electroencephalogram Using Fuzzy Support Vector Machine
Chapter 49. Regularized Multi-source Matrix Factorization for Diagnosis of Alzheimer’s Disease
Chapter 50. Multi-roles Graph Based Extractive Summarization
Chapter 51. Self-advised Incremental One-Class Support Vector Machines: An Application in Structural Health Monitoring
Chapter 52. Incremental Self-Organizing Maps for Collaborative Clustering
Chapter 53. Efficient Neighborhood Covering Reduction with Submodular Function Optimization
Chapter 54. Online Hidden Conditional Random Fields to Recognize Activity-Driven Behavior Using Adaptive Resilient Gradient Learning
Chapter 55. Atomic Distance Kernel for Material Property Prediction
Chapter 56. Batch Process Fault Monitoring Based on LPGD-kNN and Its Applications in Semiconductor Industry
Chapter 57. Large Scale Image Classification Based on CNN and Parallel SVM
Chapter 58. Malware Detection Using Deep Transferred Generative Adversarial Networks
Chapter 59. A Grassmannian Approach to Zero-Shot Learning for Network Intrusion Detection
Chapter 60. Selective Ensemble Random Neural Networks Based on Adaptive Selection Scope of Input Weights and Biases for Building Soft Measuring Model
Chapter 61. Semi-supervised Coefficient-Based Distance Metric Learning
Chapter 62. Improving Hashing by Leveraging Multiple Layers of Deep Networks
Chapter 63. Accumulator Based Arbitration Model for both Supervised and Reinforcement Learning Inspired by Prefrontal Cortex
Chapter 64. Energy-Balanced Distributed Sparse Kernel Machine in Wireless Sensor Network
Chapter 65. A Hybrid Evolutionary Algorithm for Protein Structure Prediction Using the Face-Centered Cubic Lattice Model
Chapter 66. Simulation Study of Physical Reservoir Computing by Nonlinear Deterministic Time Series Analysis
Chapter 67. Targets Detection Based on the Prejudging and Prediction Mechanism
Chapter 68. An Image Quality Evaluation Method Based on Joint Deep Learning
Chapter 69. Generic Pixel Level Object Tracker Using Bi-Channel Fully Convolutional Network
Chapter 70. RBNet: A Deep Neural Network for Unified Road and Road Boundary Detection
Chapter 71. Semi-supervised Multi-label Linear Discriminant Analysis
Chapter 72. Field Support Vector Regression
Chapter 73. Deep Mixtures of Factor Analyzers with Common Loadings: A Novel Deep Generative Approach to Clustering
Chapter 74. Improve Deep Learning with Unsupervised Objective

Part 2 Reinforcement Learning
Chapter 75. Adaptive Dynamic Programming for Direct Current Servo Motor
Chapter 76. An Event-Triggered Heuristic Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems
Chapter 77. Implicit Incremental Natural Actor Critic
Chapter 78. Influence of the Chaotic Property on Reinforcement Learning Using a Chaotic Neural Network
Chapter 79. Average Reward Reinforcement Learning for Semi-Markov Decision Processes
Chapter 80. Neuro-control of Nonlinear Systems with Unknown Input Constraints
Chapter 81. Average Reward Optimization with Multiple Discounting Reinforcement Learners
Chapter 82. Finite Horizon Optimal Tracking Control for Nonlinear Discrete-Time Switched Systems
Chapter 83. Large-Scale Bandit Approaches for Recommender Systems
Chapter 84. Off-Policy Reinforcement Learning for Partially Unknown Nonzero-Sum Games
Chapter 85. Consensus Based Distributed Reinforcement Learning for Nonconvex Economic Power Dispatch in Microgrids
Chapter 86. FMR-GA – A Cooperative Multi-agent Reinforcement Learning Algorithm Based on Gradient Ascent
Chapter 87. Policy Gradient Reinforcement Learning for I/O Reordering on Storage Servers

Part 3 Big Data Analysis
Chapter 88. Profile-Based Ant Colony Optimization for Energy-Efficient Virtual Machine Placement
Chapter 89. An Iterative Model for Predicting Film Attendance
Chapter 90. Estimating VNF Resource Requirements Using Machine Learning Techniques
Chapter 91. Accelerating Core Decomposition in Large Temporal Networks Using GPUs
Chapter 92. Pulsar Bayesian Model: A Comprehensive Astronomical Data Fitting Model
Chapter 93. Assessing the Performance of Deep Learning Algorithms for Newsvendor Problem
Chapter 94. A Small Scale Multi-Column Network for Aesthetic Classification Based on Multiple Attributes