NDLI: Extreme learning machines: a survey

Content Provider	SpringerLink
Author	Huang, Guang Bin Wang, Dian Hui Lan, Yuan
Copyright Year	2011
Abstract	Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques, neural networks and support vector machines (SVMs) have been playing the dominant roles. However, it is known that both neural networks and SVMs face some challenging issues such as: (1) slow learning speed, (2) trivial human intervene, and/or (3) poor computational scalability. Extreme learning machine (ELM) as emergent technology which overcomes some challenges faced by other techniques has recently attracted the attention from more and more researchers. ELM works for generalized single-hidden layer feedforward networks (SLFNs). The essence of ELM is that the hidden layer of SLFNs need not be tuned. Compared with those traditional computational intelligence techniques, ELM provides better generalization performance at a much faster learning speed and with least human intervene. This paper gives a survey on ELM and its variants, especially on (1) batch learning mode of ELM, (2) fully complex ELM, (3) online sequential ELM, (4) incremental ELM, and (5) ensemble of ELM.
Starting Page	107
Ending Page	122
Page Count	16
File Format	PDF
ISSN	18688071
Journal	International Journal of Machine Learning and Cybernetics
Volume Number	2
Issue Number	2
e-ISSN	1868808X
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-05-25
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Extreme learning machine Support vector machine ELM kernel ELM feature space Ensemble Incremental learning Online sequential learning Systems Biology Pattern Recognition Statistical Physics, Dynamical Systems and Complexity Control, Robotics, Mechatronics Computational Intelligence Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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