Proceedings of ELM-2015 Volume 1 [recurso electrónico] : Theory, Algorithms and Applications (I) / edited by Jiuwen Cao, Kezhi Mao, Jonathan Wu, Amaury Lendasse.

Efficient Batch Parallel Online Sequential Extreme Learning Machine Algorithm Based on MapReduce -- Fixed-Point Evaluation of Extreme Learning Machine for Classification -- Multi-Layer Online Sequential Extreme Learning Machine for Image Classification -- ELM Meets Urban Computing: Ensemble Urban Data For Smart City Applications -- Local and Global Unsupervised Kernel Extreme Learning Machine and Its Application in Nonlinear Process Fault Detection -- Parallel Multi-Graph Classification Using Extreme Learning Machine and MapReduce -- Extreme Learning Machine for Large-Scale Graph Classification Based on MapReduce -- The Distance-based Representative Skyline Calculation using Unsupervised Extreme Learning Machines -- Multi-label Text Categorization Using L21-NormMinimization Extreme Learning Machine -- Cluster-based Outlier Detection Using Unsupervised Extreme Learning Machines -- Segmentation of the Left Ventricle in Cardiac MRI Using an ELM Model -- Channel Estimation Based on Extreme Learning Machine for High Speed Environments -- MIMO Modeling Based on Extreme Learning Machine -- Graph Classification based on Sparse Graph Feature Selection and Extreme Learning Machine -- Time Series Prediction Based on Online Sequential Improved Error Minimized Extreme Learning Machine -- Adaptive Input Shaping for Flexible Systems using an Extreme Learning Machine Algorithm Identification -- Kernel Based Semi-supervised Extreme Learning Machine and the Application in Traffic Congestion Evaluation -- Improvement of ELM Algorithm for Multi-Object Identification in Gesture Interaction -- SVM and ELM: Who Wins? Object Recognition with Deep Convolutional Features from ImageNet -- Learning with Similarity Functions: a Novel Design for the Extreme Learning Machine -- A Semi-Supervised Low Rank Kernel Learning Algorithm via Extreme Learning Machine -- Application of Extreme Learning Machine on Large Scale Traffic Congestion Prediction -- Extreme Learning Machine-Guided Collaborative Coding for Remote Sensing Image Classification -- Distributed Weighted Extreme Learning Machine for Big Imbalanced Data Learning -- NMR Image Segmentation based on Unsupervised Extreme Learning Machine -- Annotating Location Semantic Tags in LBSN Using Extreme Learning Machine -- Feature Extraction of Motor Imagery EEG based on Extreme Learning Machine Auto-Encoder -- Multimodal Fusion using Kernel-based ELM for Video Emotion Recognition -- Equality Constrained-Optimization-Based Semi-Supervised ELM for Modeling -- Signal Strength Temporal Variation in Indoor Location Estimation Extreme Learning Machine with Gaussian Kernel Based Relevance Feedback Scheme for Image Retrieval -- Routing Tree Maintenance based on Trajectory Prediction in Mobile Sensor Networks -- Two-Stage Hybrid Extreme Learning Machine for Sequential Imbalanced Data -- Feature Selection and Modelling of a Steam Turbine from a Combined Heat and Power Plant Using ELM -- On The Construction of Extreme Learning Machine for One Class Classifier -- Record Linkage for Event Identification in XML Feeds Stream Using ELM -- Timeliness Online Regularized Extreme Learning Machine -- An Efficient High-dimensional Big Data Storage Structure Based on US-ELM -- An Enhanced Extreme Learning Machine for Efficient Small Sample Classification -- Code Generation Technology of Digital Satellite -- ELM-based Velocity Inversion for Sandstone Reservoir in Yanqi Gas-Field -- Class-Constrained Extreme Learning Machine.

This book contains some selected papers from the International Conference on Extreme Learning Machine 2015, which was held in Hangzhou, China, December 15-17, 2015. This conference brought together researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the Extreme Learning Machine (ELM) technique and brain learning. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM.