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Neural Computing and Applications

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09.03.2017 | Original Article

Improved Meta-ELM with error feedback incremental ELM as hidden nodes

verfasst von: Weidong Zou, Fenxi Yao, Baihai Zhang, Zixiao Guan

Erschienen in: Neural Computing and Applications | Ausgabe 11/2018

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Abstract

Liao et al. (Neurocomputing 128:81–87, 2014) proposed a meta-learning approach to extreme learning machine (Meta-ELM), which can obtain good generalization performance by training multiple ELMs. However, one of its open problems is overfitting when minimizing training error. In this paper, we propose an improved meta-learning model of ELM (improved Meta-ELM) to handle the problem. The improved Meta-ELM architecture is composed of some base ELMs which are error feedback incremental extreme learning machine (EFI-ELM) and the top ELM. The improved Meta-ELM includes two stages. First, each base ELM with EFI-ELM is trained on a subset of training data. Then, the top ELM learns with the base ELMs as hidden nodes. Simulation results on some artificial and benchmark datasets show that the proposed improved Meta-ELM model is more feasible and effective than Meta-ELM.

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Titel: Improved Meta-ELM with error feedback incremental ELM as hidden nodes
verfasst von: Weidong Zou
Fenxi Yao
Baihai Zhang
Zixiao Guan
Publikationsdatum: 09.03.2017
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 11/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-017-2922-y

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