NDLI: An improved extreme learning machine based on Variable-length Particle Swarm Optimization

Content Provider	IEEE Xplore Digital Library
Author	Bingxia Xue Xin Ma Gu, J. Yibin Li
Copyright Year	2013
Description	Author affiliation: Sch. of Control Sci. & Eng., Shandong Univ., Jinan, China (Bingxia Xue; Xin Ma; Yibin Li) \|\| Dept. of Electr. & Comput. Eng., Dalhousie Univ., Halifax, NS, Canada (Gu, J.)
Abstract	Extreme Learning Machine (ELM) for Single-hidden Layer Feedforward Neural Network (SLFN) has been attracting attentions because of its faster learning speed and better generalization performance than those of the traditional gradient-based learning algorithms. However, it has been proven that generalization performance of ELM classifier depends critically on the number of hidden neurons and the random determination of the input weights and hidden biases. In this paper, we propose Variable-length Particle Swarm Optimization algorithm (VPSO) for ELM to automatically select the number of hidden neurons as well as corresponding input weights and hidden biases for maximizing ELM classifier's generalization performance. Experimental results have verified that the proposed VPSO-ELM scheme significantly improves the testing accuracy of classification problems.
Sponsorship	IEEE Robot. Autom. Soc.
Starting Page	1030
Ending Page	1035
File Size	954099
Page Count	6
File Format	PDF
ISBN	9781479927449
DOI	10.1109/ROBIO.2013.6739599
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Classification algorithms Accuracy Training Testing Sociology Statistics
Content Type	Text
Resource Type	Article

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