Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 112-116.

• Intelligent Computing • Previous Articles     Next Articles

Feature Incremental Extreme Learning Machine

ZHAO Zhong-tang1,2, ZHENG Xiao-dong1   

  1. (School of Intelligent Engineering,Zhengzhou University of Aeronautics,Zhengzhou 450046,China)1;
    (Beijing Advanced Innovation Center for Intelligent Robots and Systems,Beijing Institute of Technology,Beijing 100081,China)2
  • Online:2019-11-10 Published:2019-11-20

Abstract: In different application fields of machine learning,many excellent classification models of extreme learning machine were produced.Researchers are often willing to share the structure and parameters of these models,but are reluctant to share the original training data.To solve the problem of how to use a small number of samples with new features and the existing classifier to generate a more efficient classifier,this paper proposed a feature incremental extreme learning machine,which can learn knowledge from samples with new features and improve the recognition accuracy of existing models.Test results on real world datasets show that the proposed algorithm can work effectively and improve the recognition accuracy of existing models,without the participation of previous training samples.

Key words: Incremental learning, Machine learning, Pervasive computing, Transfer learning

CLC Number: 

  • TP393
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