Extreme Learning Machines 2013: Algorithms and Applications
β Scribed by Fuchen Sun, Kar-Ann Toh, Manuel Grana Romay, Kezhi Mao (eds.)
- Publisher
- Springer International Publishing
- Year
- 2014
- Tongue
- English
- Leaves
- 224
- Series
- Adaptation, Learning, and Optimization 16
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
In recent years, ELM has emerged as a revolutionary technique of computational intelligence, and has attracted considerable attentions. An extreme learning machine (ELM) is a single layer feed-forward neural network alike learning system, whose connections from the input layer to the hidden layer are randomly generated, while the connections from the hidden layer to the output layer are learned through linear learning methods. The outstanding merits of extreme learning machine (ELM) are its fast learning speed, trivial human intervene and high scalability.
This book contains some selected papers from the International Conference on Extreme Learning Machine 2013, which was held in Beijing China, October 15-17, 2013. This conference aims to bring together the researchers and practitioners of extreme learning machine from a variety of fields including artificial intelligence, biomedical engineering and bioinformatics, system modelling and control, and signal and image processing, to promote research and discussions of βlearning without iterative tuning".
This book covers algorithms and applications of ELM. It gives readers a glance of the newest developments of ELM.
β¦ Table of Contents
Front Matter....Pages i-vi
Stochastic Sensitivity Analysis Using Extreme Learning Machine....Pages 1-12
Efficient Data Representation Combining with ELM and GNMF....Pages 13-23
Extreme Support Vector Regression....Pages 25-34
A Modular Prediction Mechanism Based on Sequential Extreme Learning Machine with Application to Real-Time Tidal Prediction....Pages 35-53
An Improved Weight Optimization and Cholesky Decomposition Based Regularized Extreme Learning Machine for Gene Expression Data Classification....Pages 55-66
A Stock Decision Support System Based on ELM....Pages 67-79
Robust Face Detection Using Multi-Block Local Gradient Patterns and Extreme Learning Machine....Pages 81-94
Freshwater Algal Bloom Prediction by Extreme Learning Machine in Macau Storage Reservoirs....Pages 95-111
ELM-Based Adaptive Live Migration Approach of Virtual Machines....Pages 113-134
ELM for Retinal Vessel Classification....Pages 135-143
Demographic Attributes Prediction Using Extreme Learning Machine....Pages 145-165
Hyperspectral Image Classification Using Extreme Learning Machine and Conditional Random Field....Pages 167-178
ELM Predicting Trust from Reputation in a Social Network of Reviewers....Pages 179-187
Indoor Location Estimation Based on Local Magnetic Field via Hybrid Learning....Pages 189-207
A Novel Scene Based Robust Video Watermarking Scheme in DWT Domain Using Extreme Learning Machine....Pages 209-225
β¦ Subjects
Computational Intelligence; Artificial Intelligence (incl. Robotics)
π SIMILAR VOLUMES
<span><p><b>This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners</b>. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from