Proceedings of ELM-2016

Description

This book contains some selected papers from the International Conference on Extreme Learning Machine 2016, which was held in Singapore, December 13-15, 2016. This conference will provide a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. Extreme Learning Machines (ELM) aims to break the barriers between the conventional artificial learning techniques and biological learning mechanism. ELM represents a suite of (machine or possibly biological) learning techniques in which hidden neurons need not be tuned. ELM learning theories show that very effective learning algorithms can be derived based on randomly generated hidden neurons (with almost any nonlinear piecewise activation functions), independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that “random hidden neurons” capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. ELM offers significant advantages over conventional neural network learning algorithms such as fast learning speed, ease of implementation, and minimal need for human intervention. ELM also shows potential as a viable alternative technique for large‐scale computing and artificial intelligence. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM. Front Matter....Pages i-xiii Earthen Archaeological Site Monitoring Data Analysis Using Kernel-based ELM and Non-uniform Sampling TFR....Pages 1-10 A Multi-valued Neuron ELM with Complex-Valued Inputs for System Identification Using FRA....Pages 11-25 Quaternion Extreme Learning Machine....Pages 27-36 Robotic Grasp Stability Analysis Using Extreme Learning Machine....Pages 37-51 Extreme Learning Machine for Intent Classification of Web Data....Pages 53-60 Reinforcement Extreme Learning Machine for Mobile Robot Navigation....Pages 61-73 Detection of Cellular Spikes and Classification of Cells from Raw Nanoscale Biosensor Data....Pages 75-87 Hot News Click Rate Prediction Based on Extreme Learning Machine and Grey Verhulst Model....Pages 89-97 Multiple Shadows Layered Cooperative Velocity Updating Particle Swarm Optimization....Pages 99-112 Short Term Prediction of Continuous Time Series Based on Extreme Learning Machine....Pages 113-127 Learning Flow Characteristics Distributions with ELM for Distributed Denial of Service Detection and Mitigation....Pages 129-143 Discovering Emergence and Bidding Behaviour in Competitive Electricity Market Using Agent-Based Simulation....Pages 145-158 Multi-kernel Transfer Extreme Learning Classification....Pages 159-170 Chinese Text Sentiment Classification Based on Extreme Learning Machine....Pages 171-181 Incremental ELMVIS for Unsupervised Learning....Pages 183-193 Predicting Huntington’s Disease: Extreme Learning Machine with Missing Values....Pages 195-206 Deep-Learned and Hand-Crafted Features Fusion Network for Pedestrian Gender Recognition....Pages 207-215 Facial Landmark Detection via ELM Feature Selection and Improved SDM....Pages 217-228 Online Sequential Extreme Learning Machine with Under-Sampling and Over-Sampling for Imbalanced Big Data Classification....Pages 229-239 An Automatic Identification System (AIS) Database for Maritime Trajectory Prediction and Data Mining....Pages 241-257 Back Propagation Convex Extreme Learning Machine....Pages 259-272 Data Fusion Using OPELM for Low-Cost Sensors in AUV....Pages 273-285