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Symmetric extreme learning machine

  • Extreme Learning Machine's Theory & Application
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Abstract

Extreme learning machine (ELM) can be considered as a black-box modeling approach that seeks a model representation extracted from the training data. In this paper, a modified ELM algorithm, called symmetric ELM (S-ELM), is proposed by incorporating a priori information of symmetry. S-ELM is realized by transforming the original activation function of hidden neurons into a symmetric one with respect to the input variables of the samples. In theory, S-ELM can approximate N arbitrary distinct samples with zero error. Simulation results show that, in the applications where there exists the prior knowledge of symmetry, S-ELM can obtain better generalization performance, faster learning speed, and more compact network architecture.

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Acknowledgments

This work was supported by the National High-Technologies Research and Development Program of China (2006AA04Z184), the National Natural Science Foundation of China under Grant No. 10901139, 60911130510, 60874029, and the Public Benefit Technologies R & D Program of Science and Technology Department of Zhejiang Province under Grant No. 2011C21020.

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Authors and Affiliations

  1. School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, 310027, Zhejiang, China

    Xueyi Liu & Ping Li

  2. Department of Mathematics, Zhejiang University, Hangzhou, 310027, China

    Chuanhou Gao

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  1. Xueyi Liu
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  2. Ping Li
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  3. Chuanhou Gao
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Correspondence to Ping Li.

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Liu, X., Li, P. & Gao, C. Symmetric extreme learning machine. Neural Comput & Applic 22, 551–558 (2013). https://doi.org/10.1007/s00521-012-0859-8

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  • DOI: https://doi.org/10.1007/s00521-012-0859-8

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