Extreme Learning Machines 2014

The first call for papers is available.

Extreme Learning Machines (ELM) aim to break the barriers between the conventional artificial learning techniques and biological learning mechanism. ELM represents a suite of machine learning techniques in which hidden neurons need not be tuned. ELM learning theories show that hidden neurons (with almost any nonlinear activation functions) can be randomly generated independent of training data and application environments, which has recently been confirmed with concrete biological evidences. ELM theories and algorithms argue that “random hidden neurons” capture the essence of some brain learning mechanism as well as the intuitive sense that the efficiency of brain learning need not rely on computing power of neurons. This may somehow hint at possible reasons why brain is more intelligent and effective than computers. ELM offers significant advantages such as fast learning speed, ease of implementation, and minimal human intervention. ELM has good potential as a viable alternative technique for large‐scale computing and artificial intelligence.

The main theme of ELM2014 is: Big Data Analytics and Machine Learning.

Organized by Nanyang Technological University, and co‐organized by Tsinghua University and National University of Singapore, ELM2014 will be held in the beautiful island‐country of Singapore. 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 and brain learning.

All submitted papers will be thoroughly reviewed to maintain a certain level of quality and standard in order to be considered for ELM2014.  Accepted papers need to be presented at the conference. Selected papers will be recommended for further review for publication consideration in special issues of reputable ISI indexed international journals. Other accepted papers will be published in special edited ELM2014 conference Proceedings volumes by Springer-Verlag. Such submissions should not have been submitted elsewhere and they are not currently under review by other conferences or journals.

Topics of interest

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


  • Universal approximation and convergence
  • Robustness and stability analysis


  • Real-time learning, reasoning and cognition
  • Sequential/incremental learning and kernel learning
  • Clustering and feature extraction/selection
  • Random projection, dimensionality reduction, and matrix factorization
  • Closed form and non‐closed form solutions
  • Multi hidden layers solutions and random networks
  • Parallel and distributed computing / cloud computing


  • Time series prediction
  • Pattern recognition
  • Web applications
  • Biometrics and Bioinformatics
  • Power systems and control engineering
  • Security and compression
  • Human computer interface and brain computer interface
  • Cognitive science/computation
  • Sentic computing / natural language processing
  • Data analytics, super / ultra large‐scale data processing