Proceedings of ELM-2015 Volume 1
Theory, Algorithms and Applications (I)
Paperback Engels 2018 9783319803449Samenvatting
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.
Specificaties
Lezersrecensies
Inhoudsopgave
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 L<sub>21</sub>-NormMinimization
Extreme Learning Machine.- Cluster-based Outlier Detection Using Unsupervised Extreme Learning Machines.- Segmentation of the Left
Ventriclein 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.</p>
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