Extreme Learning Machines (ELM) provide efficient unified solutions to generalized feedforward networks including but not limited to (both single‐hidden‐layer and multi‐ hidden‐layer) feedforward neural networks, RBF networks, and kernel learning. ELM possesses unique features to deal with regression and (multi‐class) classification tasks. Consequently, ELM offers significant advantages such as fast learning speed, ease of implementation, and minimal human intervene. ELM has good potential as a viable alternative technique for large‐scale computing and artificial intelligence.

Organized by Tsinghua University, Northeastern University, and Nanyang Technological University, ELM2013 will be held in Beijing, the capital of China. This conference will provide a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique, as well as participating in a competition on a data‐centric application problem. Details of ELM2013 competition will be announced by March 15, 2013.

All accepted papers presented in this conference will be published in special issues of reputable ISI indexed international journals or special edited book volumes to be published by Springer‐Verlag. No additional conference proceedings are provided.
Topics of interest

All the submissions must be related to ELM technique. Topics of interest include but are not limited to:

Theories

Universal approximation and convergence
Robustness and stability analysis

Algorithms

Real-time learning/reasoning
Sequential and incremental learning
Kernel based algorithms

Applications

Time series prediction
Pattern recognition
Web applications
Biometrics
Bioinformatics
Power systems
Control engineering
Security
Compression
Human computer interface
Imbalanced data processing
Data analytics
Super/ultra large-scale data processing