Proceedings of Elm-2015 Volume 1: Theory, Algorithms and Applications (I)

ISBN-13: 9783319803449 / Angielski / Miękka / 2018 / 532 str.

Jiuwen Cao; Kezhi Mao; Jonathan Wu

Spis treści

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.

 

 

Blurb

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.