Neural Computing and Applications

, Volume 27, Issue 1, pp 59–66 | Cite as

Applying a new localized generalization error model to design neural networks trained with extreme learning machine

  • Qiang LiuEmail author
  • Jianping Yin
  • Victor C. M. Leung
  • Jun-Hai Zhai
  • Zhiping Cai
  • Jiarun Lin
Extreme Learning Machine and Applications


High accuracy and low overhead are two key features of a well-designed classifier for different classification scenarios. In this paper, we propose an improved classifier using a single-hidden layer feedforward neural network (SLFN) trained with extreme learning machine. The novel classifier first utilizes principal component analysis to reduce the feature dimension and then selects the optimal architecture of the SLFN based on a new localized generalization error model in the principal component space. Experimental and statistical results on the NSL-KDD data set demonstrate that the proposed classifier can achieve a significant performance improvement compared with previous classifiers.


Extreme learning machine Principal component analysis Architecture selection algorithm Localized generalization error model 


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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Qiang Liu
    • 1
    Email author
  • Jianping Yin
    • 2
  • Victor C. M. Leung
    • 3
  • Jun-Hai Zhai
    • 4
  • Zhiping Cai
    • 1
  • Jiarun Lin
    • 1
  1. 1.School of ComputerNational University of Defense TechnologyChangshaChina
  2. 2.State Key Laboratory of High Performance ComputingNational University of Defense TechnologyChangshaChina
  3. 3.Department of Electrical and Computer EngineeringUniversity of British ColumbiaVancouverCanada
  4. 4.Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer ScienceHebei UniversityBaodingChina

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