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Soft Computing

, Volume 22, Issue 2, pp 677–689 | Cite as

Information discriminative extreme learning machine

  • Deqin Yan
  • Yonghe Chu
  • Haiying Zhang
  • Deshan Liu
Methodologies and Application

Abstract

Extreme learning machine (ELM) has become one of the new research hotspots in the field of pattern recognition and machine learning. However, the existing extreme learning machine algorithms cannot better use identification information of data. Aiming at solving this problem, we propose a regularized extreme learning machine (algorithm) based on discriminative information (called IELM). In order to evaluate and verify the effectiveness of the proposed method, experiments use widely used image data sets. The comparative experimental results show that the proposed algorithm in the paper can significantly improve the classification performance and generalization ability of ELM.

Keywords

Extreme learning machine Pattern recognition Identification information 

Notes

Acknowledgments

This study was funded by National Natural Science Foundation of China (Grant Number 61105085) and Science Foundation of education ministry of Liaoning province (L2014427).

Compliance with ethical standards

Conflict of interest

Author Deqin Yan declares that he has no conflict of interest. Author Yonghe Chu declares that he has no conflict of interest. Author Haiying Zhang declares that she has no conflict of interest. Author Deshan Liu declares that he has no conflict of interest.

Ethical standard

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Human and animal rights

All applicable international, national, or institutional guidelines for the care and use of animals were followed.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Deqin Yan
    • 1
  • Yonghe Chu
    • 1
  • Haiying Zhang
    • 1
  • Deshan Liu
    • 1
  1. 1.School of Computer and Information TechnologyLiaoning Normal UniversityDalianChina

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