A Robust Online Sequential Extreme Learning Machine

Authors:
Minh-Tuan T. Hoang;Hieu T. Huynh;Nguyen H. Vo;Yonggwan Won
Affiliations:
Department of Computer Engineering, Chonnam National University, 300 Yongbong-Dong, Buk-Gu, Kwangju 500-757, Korea;Department of Computer Engineering, Chonnam National University, 300 Yongbong-Dong, Buk-Gu, Kwangju 500-757, Korea;Department of Computer Engineering, Chonnam National University, 300 Yongbong-Dong, Buk-Gu, Kwangju 500-757, Korea;Department of Computer Engineering, Chonnam National University, 300 Yongbong-Dong, Buk-Gu, Kwangju 500-757, Korea
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Abstract

Online-sequential extreme learning machine (OS-ELM) shows a good solution to online learning using extreme learning machine approach for single-hidden-layer feedforward network. However, the algorithm tends to be data-dependent, i.e. the bias values need to be adjusted depending on each particular problem. In this paper, we propose an enhancement to OS-ELM, which is referred to as robust OS-ELM (ROS-ELM). ROS-ELM has a systematic method to select the bias that allows the bias to be selected following the input weights. Hence, the proposed algorithm works well for every benchmark dataset. ROS-ELM has all the pros of OS-ELM, i.e. the capable of learning one-by-one, chunk-by-chunk with fixed or varying chunk size. Moreover, the performance of the algorithm is higher than OS-ELM and it produces a better generalization performance with benchmark datasets.