Information discriminative extreme learning machine
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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.
KeywordsExtreme learning machine Pattern recognition Identification information
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.
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 was obtained from all individual participants included in the study.
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