Improved Extreme Learning Machine based on the Sensitivity Analysis

Extreme learning machine and its improved ones is weak in some points, such as computing complex, learning error and so on. After deeply analyzing, referencing the importance of hidden nodes in SVM, an novel analyzing method of the sensitivity is proposed which meets people's cognitive habits....

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2018-03, Vol.320 (1), p.12015
Main Authors: Cui, Licheng, Zhai, Huawei, Wang, Benchao, Qu, Zengtang
Format: Article
Language:English
Subjects:
Artificial neural networks
Error analysis
Extreme learning machine
Importance
Machine learning
Nodes
Sensitivity analysis
SVM
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Summary:Extreme learning machine and its improved ones is weak in some points, such as computing complex, learning error and so on. After deeply analyzing, referencing the importance of hidden nodes in SVM, an novel analyzing method of the sensitivity is proposed which meets people's cognitive habits. Based on these, an improved ELM is proposed, it could remove hidden nodes before meeting the learning error, and it can efficiently manage the number of hidden nodes, so as to improve the its performance. After comparing tests, it is better in learning time, accuracy and so on.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/320/1/012015