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A new method of online extreme learning machine based on hybrid kernel function

  • S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems
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Abstract

Computational complexity and sample selection are two main factors that limited the performance of online sequential extreme learning machines (OS-ELMs). This paper proposes a new model that introduces the concept of hybrid kernel and sample selection method based on an online learning model using a membership function. In other words, an online sequential extreme learning machine based on a hybrid kernel function (HKOS-ELM) is presented. The algorithm only calculates the kernel function to determine the final output function, mostly solving the computational complexity of the algorithm. The hybrid kernel function proposed in this paper has the advantages of strong learning ability and good generalization performance of single kernel function. Based on the classification essence of the OS-ELM classification, the membership function is introduced into the sample selection to remove the noise point and the outlier point. The experimental results showed that the HKOS-ELM algorithm adding the membership degree with mixed kernel functions preserves the advantages of kernel functions and online learning and improves the classification performance of the system.

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Acknowledgements

The paper is supported in part by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61272036, 61702213.

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Authors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Senyue Zhang, Wenan Tan & Qingjun Wang

  2. Shenyang Aerospace University, Shenyang, China

    Senyue Zhang & Qingjun Wang

  3. Jilin University of Finance and Economics, Changchun, China

    Nan Wang

Authors
  1. Senyue Zhang
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  2. Wenan Tan
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  3. Qingjun Wang
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  4. Nan Wang
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Correspondence to Senyue Zhang.

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Zhang, S., Tan, W., Wang, Q. et al. A new method of online extreme learning machine based on hybrid kernel function. Neural Comput & Applic 31, 4629–4638 (2019). https://doi.org/10.1007/s00521-018-3629-4

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