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Improved Extreme Learning Machine and Its Application in Image Quality Assessment
Joint Authors
Li, Chaofeng
Liu, Xingyang
Zhang, Lidong
Yang, Hong
Mao, Li
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-05-22
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Extreme learning machine (ELM) is a new class of single-hidden layer feedforward neural network (SLFN), which is simple in theory and fast in implementation.
Zong et al.
propose a weighted extreme learning machine for learning data with imbalanced class distribution, which maintains the advantages from original ELM.
However, the current reported ELM and its improved version are only based on the empirical risk minimization principle, which may suffer from overfitting.
To solve the overfitting troubles, in this paper, we incorporate the structural risk minimization principle into the (weighted) ELM, and propose a modified (weighted) extreme learning machine (M-ELM and M-WELM).
Experimental results show that our proposed M-WELM outperforms the current reported extreme learning machine algorithm in image quality assessment.
American Psychological Association (APA)
Mao, Li& Zhang, Lidong& Liu, Xingyang& Li, Chaofeng& Yang, Hong. 2014. Improved Extreme Learning Machine and Its Application in Image Quality Assessment. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-471255
Modern Language Association (MLA)
Mao, Li…[et al.]. Improved Extreme Learning Machine and Its Application in Image Quality Assessment. Mathematical Problems in Engineering No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-471255
American Medical Association (AMA)
Mao, Li& Zhang, Lidong& Liu, Xingyang& Li, Chaofeng& Yang, Hong. Improved Extreme Learning Machine and Its Application in Image Quality Assessment. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-471255
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-471255