# Neural Information Processing, Part I

- Length: 936 pages
- Edition: 1st ed. 2017
- Language: English
- Publisher: Springer
- Publication Date: 2017-11-29
- ISBN-10: 3319700863
- ISBN-13: 9783319700861

**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