个人信息Personal Information
教授
博士生导师
硕士生导师
主要任职:计算机科学与技术学院院长
其他任职:计算机学院院长
性别:男
毕业院校:西安电子科技大学
学位:博士
所在单位:计算机科学与技术学院
学科:计算机应用技术
联系方式:E-Mail: zhangq@dlut.edu.cn
电子邮箱:zhangq@dlut.edu.cn
The memory degradation based online sequential extreme learning machine
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论文类型:期刊论文
发表时间:2018-01-31
发表刊物:NEUROCOMPUTING
收录刊物:SCIE、EI
卷号:275
页面范围:2864-2879
ISSN号:0925-2312
关键字:Online learning; Extreme learning machine; Memory factor; Similarity
摘要:In online learning, the contribution of old samples to a model decreases as time passes, and old samples gradually become invalid. Although the Online Sequential Extreme Learning Machine (OS-ELM) can avoid the repetitive training of old samples, invalid samples are still used, which goes against improving the accuracy of an OS-ELM model. The Online Sequence Extreme Learning Machine with Forgetting Mechanism (FOS-ELM) timely discards invalid samples, but it does not consider the differences among valid samples and then has the limitation on boosting the accuracy and generalization. To solve this issue, the Memory Degradation Based OS-ELM (MDOS-ELM) is proposed in this paper. The MDOS-ELM adjusts the weights of the old and new samples in real time by a self-adaptive memory factor, and simultaneously discards invalid samples. The self-adaptive memory factor is determined by two elements. One is the similarity between the new and old samples, and the other is the prediction errors of the current training samples on the previous model. The performance of the proposed MDOS-ELM is validated on both regression and classification datasets which include an artificial dataset and twenty-two real-world dataset. The results demonstrate that the MDOS-ELM model outperforms the OS-ELM and the FOS-ELM models on the accuracy and generalization. (C) 2017 Elsevier B.V. All rights reserved.