Extreme Learning Machines 2013: Algorithms and Applications

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 learning without iterative tuning".

This book covers algorithms and applications of ELM. It gives readers a glance of the newest developments of ELM.

Explains the most recent developments of Extreme Learning Machines Includes theories and algorithms such as universal approximation and convergence, robustness and stability analysis, real-time learning/reasoning, sequential and incremental learning, and kernel based algorithms Proceedings of the International Conference on Extreme Learning Machines (ELM2013), Beijing, October 15-17, 2013 Includes supplementary material: sn.pub/extras

Inhalt
Freshwater Algal Bloom Prediction by Extreme Learning Machine in Macau Storage Reservoirs.- A Novel Scene Based Robust Video Watermarking Scheme in DWT Domain Using Extreme Learning Machine.- Stochastic Sensitivity Analysis using Extreme Learning Machine.