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Smoothing L0 Regularization for Extreme Learning Machine
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-06
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Extreme learning machine (ELM) has been put forward for single hidden layer feedforward networks.
Because of its powerful modeling ability and it needs less human intervention, the ELM algorithm has been used widely in both regression and classification experiments.
However, in order to achieve required accuracy, it needs many more hidden nodes than is typically needed by the conventional neural networks.
This paper considers a new efficient learning algorithm for ELM with smoothing L0 regularization.
A novel algorithm updates weights in the direction along which the overall square error is reduced the most and then this new algorithm can sparse network structure very efficiently.
The numerical experiments show that the ELM algorithm with smoothing L0 regularization has less hidden nodes but better generalization performance than original ELM and ELM with L1 regularization algorithms.
American Psychological Association (APA)
Fan, Qinwei& Liu, Ting. 2020. Smoothing L0 Regularization for Extreme Learning Machine. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1202047
Modern Language Association (MLA)
Fan, Qinwei& Liu, Ting. Smoothing L0 Regularization for Extreme Learning Machine. Mathematical Problems in Engineering No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1202047
American Medical Association (AMA)
Fan, Qinwei& Liu, Ting. Smoothing L0 Regularization for Extreme Learning Machine. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1202047
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202047