Extreme Learning Machines on High Dimensional and Large Data Applications: A Survey
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-07-02
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Extreme learning machine (ELM) has been developed for single hidden layer feedforward neural networks (SLFNs).
In ELM algorithm, the connections between the input layer and the hidden neurons are randomly assigned and remain unchanged during the learning process.
The output connections are then tuned via minimizing the cost function through a linear system.
The computational burden of ELM has been significantly reduced as the only cost is solving a linear system.
The low computational complexity attracted a great deal of attention from the research community, especially for high dimensional and large data applications.
This paper provides an up-to-date survey on the recent developments of ELM and its applications in high dimensional and large data.
Comprehensive reviews on image processing, video processing, medical signal processing, and other popular large data applications with ELM are presented in the paper.
American Psychological Association (APA)
Cao, Jiuwen& Lin, Zhiping. 2015. Extreme Learning Machines on High Dimensional and Large Data Applications: A Survey. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1072907
Modern Language Association (MLA)
Cao, Jiuwen& Lin, Zhiping. Extreme Learning Machines on High Dimensional and Large Data Applications: A Survey. Mathematical Problems in Engineering No. 2015 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1072907
American Medical Association (AMA)
Cao, Jiuwen& Lin, Zhiping. Extreme Learning Machines on High Dimensional and Large Data Applications: A Survey. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1072907
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1072907