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Extend semi-supervised ELM and a frame work

  • Extreme Learning Machine and Applications
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Abstract

Extreme learning machines (ELM) is a state-of-the-art classification algorithm. Many applications and ELM modified versions have been proposed in recent years. We propose a frame work of semi-supervised ELM (SELM) based on SELM (Liu et al. in Neurocomputing 74:2566–2572, 2011). In this paper, we research the SELM intensively and extend SELM to ESELM (extended SELM). Compared with SELM, ESLEM considers the empirical risk and structural risk at the same time. Furthermore, we integrate LLE graph/\(l\)1 graph which were proposed recently into ESELM and compare them to the classic Laplace graph. The real-world datasets experiments indicate the effectiveness of ESELM.

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Acknowledgments

This work was supported by National Natural Science Foundation of P.R. China (61173163, 51105052, 61370200) and Liaoning Provincial Natural Science Foundation of China (Grant No. 201102037). The authors would like to thank the reviewers for their comments which has improved the quality of the work.

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Authors and Affiliations

  1. School of Computer Science and Technology, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, Liaoning, China

    ShengLan Liu, Lin Feng, HuiBing Wang & Yao Xiao

  2. School of Innovation Experiment, Dalian University of Technology, Dalian, 116024, Liaoning, China

    ShengLan Liu & Lin Feng

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  1. ShengLan Liu
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  2. Lin Feng
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  3. HuiBing Wang
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  4. Yao Xiao
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Correspondence to Lin Feng.

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Liu, S., Feng, L., Wang, H. et al. Extend semi-supervised ELM and a frame work. Neural Comput & Applic 27, 205–213 (2016). https://doi.org/10.1007/s00521-014-1713-y

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  • DOI: https://doi.org/10.1007/s00521-014-1713-y

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