Proceedings of ELM-2014 Volume 2

Applications
Softcover Nachdruck of the original 1. Auflage 2015
ISBN: 978-3-319-36685-2
Verlag: Springer International Publishing

Buch, Englisch, Band 4, 400 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 6146 g

Reihe: Proceedings in Adaptation, Learning and Optimization

This book contains some selected papers from the International Conference on Extreme Learning Machine 2014, which was held in Singapore, December 8-10, 2014. This conference brought together the researchers and practitioners of Extreme Learning Machine (ELM) from a variety of fields to promote research and development of “learning without iterative tuning”. The book covers theories, algorithms and applications of ELM. It gives the readers a glance of the most recent advances of ELM.

Zielgruppe

Research

Autoren/Hrsg.

Cao, Jiuwen

Mao, Kezhi

Toh, Kar-Ann

Cambria, Erik

Man, Zhihong

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Using Extreme Learning Machine for Filamentous Bulking Prediction and Forecast in Wastewater Treatment Plants.- Extreme Learning Machine for Linear Dynamical Systems Classification: Application to Human Activity Recognition.- Lens Distortion Correction Using ELM.- Pedestrian Detection in Thermal Infrared Image using Extreme Learning Machine.- Dynamic Texture Video Classification Using Extreme Learning Machine.- Uncertain XML Documents Classification Using Extreme Learning Machine.- Encrypted traffic identification based on randomness sparse feature and extreme learning machine.- Network Intrusion Detection Based on Extreme Learning Machine.- A Study on Three-dimensional Motion History Image and Extreme Learning Machine Oriented Body Movements Trajectory Recognition.- An Improved ELM Algorithm for the Measurement of Hot Metal Temperature in Blast Furnace.- Wi-Fi and Motion Sensors based Indoor Localization Combining ELM and Particle Filter.- Online Sequential Extreme Learning Machine for Watermarking.- Adaptive neural control of quadrotor helicopter with extreme learning machine.- Keyword Search on Probabilistic XML Data based on ELM.- A Novel HVS Based Gray Scale Image Watermarking Scheme Using Fast Fuzzy - ELM Hybrid Architecture.- Wearable EyeGlass based Fall Detection using Weighted ELM.- Concise Feature Extraction based ELM for Active Service Quality Prediction.- Multi-class AdaBoost ELM and Its Application in LBP Based Face Recognition.- Detecting Copy Directions among Programs Using Extreme Learning Machines.- Extreme learning machine for reservoir parameter estimation in heterogeneous reservoir.- Multifault Diagnosis for Rolling Element Bearings Based on Extreme Learning Machine.- Gradient-based No-Reference Image Blur Assessment Using Extreme Learning Machine.- RFID Enabled Indoor Positioning for Real-time Manufacturing Execution System based on OS-ELM.- An Online Sequential Extreme Learning Machine for Tidal Prediction based on Improved Gath-Geva Fuzzy Segmentation.- Recognition of Human Stair Ascent and Descent Activities based on Extreme Learning Machine.- ELM Based Dynamic Modeling for Online Prediction of Content in Molten Iron.-  Distributed Learning over Massive XML Documents in ELM Feature Space.- Hyperspectral Image Nonlinear Unmixing by Ensemble ELM Regression.- Text-Image Separation and Indexing in Historic Patent Document Image Based on Extreme Learning Machine.- Anomaly Detection with ELM-based Visual Attribute and Spatio-temporal Pyramid.- Modelling and Prediction of Surface Roughness and Power Consumption using Parallel Extreme Learning Machine based Particle Swarm Optimization.- OS-ELM based Emotion Recognition for Empathetic Elderly Companion.- Access Behavior Prediction in Distributed StorageSystem using Regularized Extreme Learning Machine.- ELM Based Fast CFD Model with Sensor Adjustment.- Melasma Image Segmentation Using Extreme Learning Machine.- Detection of Drivers' Distraction Using Semi-Supervised Extreme Learning Machine.- Driver Workload Detection in On-road Driving Environment using Machine Learning.