4th International Conference on Extreme Learning Machines (ELM)

[Erik Cambria]

Apologies for cross posting.

Please consider submitting to ELM13 (http://sentic.net/elm), to be held in Beijing this October.

Extreme Learning Machines (ELM) provide efficient unified solutions to generalized feedforward networks including but not limited to feedforward neural 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 least human intervene. ELM has good potential as a viable alternative technique for large-scale computing and AI. The ELM symposiumprovides 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. All accepted papers presented in this symposium are published in special issues of Information Sciences, IEEE Intelligent Systems, Cognitive Computation, Neurocomputing, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems and World Wide Web - Internet and Web Information Systems (no additional symposium proceedings are provided). 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
• Cognitive science/computation
• Power systems
• Control engineering
• Security
• Compression
• Human computer interface
• Sentic computing / Natural language processing
• Imbalanced data processing
• Data analytics, Super/ultra large-scale data processing
_______________________________
Erik Cambria, PhD
康文涵
Research Scientist

Temasek Laboratories
Cognitive Science Programme
National University of Singapore
5A Engineering Drive 1, Singapore 117411

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