NDLI: An improved Extreme Learning Machine

Content Provider	IEEE Xplore Digital Library
Author	Ke Hai-sen Huang Xiao-lan
Copyright Year	2013
Description	Author affiliation: Coll. of Mech. & Electr. Eng., China Jiliang Univ., Hangzhou, China (Ke Hai-sen; Huang Xiao-lan)
Abstract	The input weight and the bias of hidden layer nodes are randomly generated in the training process of the traditional Extreme Learning Machine (ELM), which is simple and with no need for repeated iteration, thus the model training speed increases significantly. However, this algorithm model possesses a defect that it's difficult to choose a reasonable network structure for users due to the parameters which are randomly generated each time can not guarantee high accuracy. For the requirements on rapidity and accuracy of ELM, an improved ELM model is proposed in this paper, the sine function data and its variants function data are adopted to be as the training sample and testing sample. The algorithm execution of generating two paremeters randomly has been circulated n times by defining cycle number n, and then a group of parameters with highest accuracy are automatically selected from the n cycles. Experimental results show that, compared with traditional ELM, the model accuracy of the proposed scheme improves greatly; then compared with BP neural network, the improved ELM model in this paper has great advantage in training speed, and also can achieve very high precision in accuracy.
Starting Page	3232
Ending Page	3237
File Size	499632
Page Count	6
File Format	PDF
ISBN	9789881563835
ISSN	19341768
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	Training Electrical engineering Accuracy Abstracts Educational institutions Data models Electronic mail Improved ELM Training time
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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