Del 0 - Proceedings in Adaptation, Learning and Optimization

Proceedings of ELM-2015 Volume 2

Theory, Algorithms and Applications (II)

Inbunden, Engelska, 2016

AvJiuwen Cao,Kezhi Mao,Jonathan Wu,Amaury Lendasse

2 769 kr

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This book contains some selected papersfrom 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 andexchange R&D experience on both theoretical studies and practicalapplications of the Extreme Learning Machine (ELM) technique and brainlearning.This book covers theories, algorithms adapplications of ELM. It gives readers a glance of the most recent advances ofELM.

Produktinformation

Utgivningsdatum2016-01-25
Mått155 x 235 x 34 mm
Vikt951 g
FormatInbunden
SpråkEngelska
SerieProceedings in Adaptation, Learning and Optimization
Antal sidor516
Upplaga16001
FörlagSpringer International Publishing AG
ISBN9783319283722

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Large-Scale Scene Recognition based on Extreme Learning Machines.- Partially Connected ELM for Fast and Effective Scene Classification Optimization.- Two-Layer Extreme Learning Machine for Dimension Reduction.- Distributed Extreme Learning Machine with Alternating Direction Method of Multiplier.- An Adaptive Online Sequential Extreme Learning Machine for Real-Time Tidal Level Prediction.- Optimization of Outsourcing ELM problems in Cloud Computing from Multi-Parties.- H-MRST: A Novel Framework For Support Uncertain Data Range Query Using ELM.- The SVM-ELM Model based on Particle Swarm Optimization.- ELM-ML: Study on Multi-Label Classification using Extreme Learning Machine.- Sentiment Analysis of Chinese Micro Blog based on DNN and ELM and Vector Space Model.- Self Forward and Information Dissemination Prediction Research in SINA Microblog Using ELM.- Sparse Coding Extreme Learning Machine for Classification.- Continuous Top-K Remarkable comments Over Textual Streaming Data UsingELM.- ELM based Representational Learning for Fault Diagnosis of Wind Turbine Equipment.- Prediction of Pulp Concentration Using Extreme Learning Machine.- Rational and Self-Adaptive Evolutionary Extreme Learning Machine for Electricity Price Forecast.- Contractive ML-ELM for Invariance Robust Feature Extraction.- Automated Human Facial Expression Recognition Using Extreme Learning Machines.- Multi-Modal Deep Extreme Learning Machine for Robotic Grasping Recognition.- Denoising Deep Extreme Learning Machines for Sparse Representation.- Extreme Learning Machine based Point-of-Interest Recommendation in Location-based Social Networks.- The Granule-Based Interval Forecast for Wind Speed.- KELM : An Improved K-means Clustering Method using Extreme Learning Machine.- Wind Power Ramp Events Classification using Extreme Learning Machines.- Facial Expression Recognition Based on Ensemble Extreme Learning Machine with Eye Movements Information.- Correlation between Extreme Learning Machine andEntorhinal Hippocampal System.- RNA Secondary Structure Prediction using Extreme Learning Machine with Clustering Under-Sampling Technique.- Multi-Instance Multi-label learning by Extreme Learning Machine.- A Randomly Weighted Gabor Network for Visual-Thermal Infrared Face Recognition.- Dynamic Adjustment of Hidden Layer Structure for Convex Incremental Extreme Learning Machine.- ELMVIS+: Improved Nonlinear Visualization Technique using Cosine Distance and Extreme Learning Machines.- On Mutual Information over non-Euclidean Spaces, Data Mining and Data Privacy Levels.- Probabilistic Methods for Multiclass Classification Problems.- A Pruning Ensemble Model of Extreme Learning Machine with L1/2 Regularizer.- Evaluating Confidence Intervals for ELM Predictions.- Real-Time Driver Fatigue Detection Based on ELM.- A High Speed Multi-label Classifier based on Extreme Learning Machines.- Image Super-Resolution by PSOSEN of Local Receptive Fields Based Extreme Learning Machine.- Sparse ExtremeLearning Machine for Regression.- WELM：Extreme Learning Machine with Wavelet Dynamic Co-Movement Analysis in High-Dimensional Time Series.