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山东大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (2): 1-5.doi: 10.6040/j.issn.1672-3961.2.2015.030

• 机器学习与数据挖掘 •    下一篇

核心集径向基函数极限学习机

翟俊海1,张素芳2,胡文祥3,王熙照1   

  1. 1.河北大学河北省机器学习与计算智能重点试验室, 河北 保定 071002; 2.中国气象局气象干部培训学院河北分院, 河北 保定 071000;3. 河北大学计算机科学与技术学院, 河北 保定 071002
  • 收稿日期:2015-05-18 出版日期:2016-04-20 发布日期:2015-05-18
  • 作者简介:翟俊海(1964— ),男,河北易县人,教授,博士,主要研究方向为机器学习与数据挖掘.E-mail:mczjh@126.com
  • 基金资助:
    国家自然科学基金资助项目(71371063,61170040);河北省自然科学基金资助项目(F2013201220,F2013201110);河北省高等学校科学技术研究重点资助项目(ZD20131028)

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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摘要： 径向基函数极限学习机(radial basis function-extreme learning machine, RBF-ELM)中的两个参数都随机地生成,这导致RBF-ELM算法的不稳定性问题。另外,对于不同的数据集,难于确定隐含层结点的个数。针对RBF-ELM的这两个问题,提出了一种改进算法。首先用核心集方法选择重要的样例,然后用选择的样例初始化中心参数,宽度参数采用随机化方法初始化。该算法不仅可以在一定程度上解决RBF-ELM的不稳定性问题,而且可以确定隐含层结点的个数。试验结果表明:该算法优于RBF-ELM。

关键词: 核函数, 极限学习机, 核心集, 径向基函数, 随机映射

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

中图分类号: 

  • TP181
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