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Titolo: | Proceedings of ELM-2015 Volume 2 : Theory, Algorithms and Applications (II) / / edited by Jiuwen Cao, Kezhi Mao, Jonathan Wu, Amaury Lendasse |
Pubblicazione: | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
Edizione: | 1st ed. 2016. |
Descrizione fisica: | 1 online resource (507 p.) |
Disciplina: | 006.3 |
Soggetto topico: | Computational intelligence |
Artificial intelligence | |
Bioinformatics | |
Data mining | |
Computational Intelligence | |
Artificial Intelligence | |
Computational Biology/Bioinformatics | |
Data Mining and Knowledge Discovery | |
Persona (resp. second.): | CaoJiuwen |
MaoKezhi | |
WuJonathan | |
LendasseAmaury | |
Note generali: | Description based upon print version of record. |
Nota di bibliografia: | Includes bibliographical references and index. |
Nota di contenuto: | 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 Using ELM -- 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 and Entorhinal 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 Extreme Learning Machine for Regression -- WELM:Extreme Learning Machine with Wavelet Dynamic Co-Movement Analysis in High-Dimensional Time Series. |
Sommario/riassunto: | 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. . |
Titolo autorizzato: | Proceedings of ELM-2015 Volume 2 |
ISBN: | 3-319-28373-1 |
Formato: | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione: | Inglese |
Record Nr.: | 9910254259303321 |
Lo trovi qui: | Univ. Federico II |
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