Volume 11 Number 8 (Aug. 2016)
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JSW 2016 Vol.11(8): 745-755 ISSN: 1796-217X
doi: 10.17706/jsw.11.8.745-755

An Improved Local Coupled Extreme Learning Machine

Chong Liu1, Bing Qiang Wang2*, Xiao Lan Wang2, Yu Lin He3*, Rana Aamir Raza Ashfaq3, 4
1Office of Scientific Research and Practical Training, Cangzhou Technical College, Cangzhou 061001, China.
2Department of Information Engineering, Cangzhou Technical College, Cangzhou 061001, China.
3College of Computer Science & Software Engineering, Shenzhen University, Shenzhen 518060, China.
4Department of Computer Science, Bahauddin Zakariya University, Multan, Pakistan.

Abstract—Local Coupled Extreme Learning Machine (LCELM) is a recently-proposed variant of ELM, which assigns an address for each hidden-layer node and activates the hidden-layer node when its activated degree is less than a given threshold. In this paper, an improved version of LCELM is proposed by developing a new way to initialize the address for each hidden-layer node and calculating the activated degree of hidden-layer node with Gaussian kernel. The experimental comparison with ELM and LCELM demonstrates the feasibility and effectiveness of improve LCELM which obtains the higher testing accuracy without significantly increasing the training time of ELM.

Index Terms—Extreme learning machine, address of hidden-layer node, window function, Gaussian kernel.

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Cite: Chong Liu, Bing Qiang Wang, Xiao Lan Wang, Yu Lin He, Rana Aamir Raza Ashfaq,, "An Improved Local Coupled Extreme Learning Machine," Journal of Software vol. 11, no. 8, pp. 745-755, 2016.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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