JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2016, Vol. 46 ›› Issue (2): 1-5.doi: 10.6040/j.issn.1672-3961.2.2015.030

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Radial basis function extreme learning machine based on core sets

ZHAI Junhai1, ZHANG Sufang2, HU Wenxiang3, WANG Xizhao1   

  1. 1. Hebei Province Key Laboratory of Machine Learning and Computational Intelligence, Hebei University, Baoding 071002, Hebei, China;
    2. Hebei Branch of Meteorological Cadres Training Institute, China Meteorological Administration, Baoding 071000, Hebei, China;
    3. School of Computer Science and Technology, Hebei University, Baoding 071002, Hebei, China
  • Received:2015-05-18 Online:2016-04-20 Published:2015-05-18

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Abstract: Radial basis function-extreme learning machine(RBF-ELM)employed randomized method to initialize the centers and widths. Randomly initialization of the two parameters led to instability of RBF-ELM. Moreover, for different data sets, it was difficult to determine the number of the hidden nodes. An improved algorithm was proposed, which firstly selected important instances with core set method, and then the centers were initialized with the selected instances, the width parameters were randomly initialized. The proposed algorithm not only could solve the problem of the instability of RBF-ELM to some extent, but also could determine the number of hidden layer nodes. Experimental results showed that the proposed algorithm outperforms RBF-ELM algorithm.

Key words: radial basis function, random mapping, kernel functions, core sets, extreme learning machine

CLC Number: 

  • TP181
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