Abstract
Although the extreme learning machine(ELM)algorithm is applied in training single hidden layer feedforward neural networks without manual intervention and its fast training speed has been recognized nowadays, there are still several problems need to be solved in this algorithm. In this paper, we propose a new improved ELM algorithm–Error Minimized and Ridge Regression ELM(ER-ELM) derived from how to select the number of hidden nodes and the way to handle with the multicollinearity. We compare the ER-ELM algorithm with other relative algorithms such as ELM et al. Simulation results show that the ER-ELM algorithm has better stability and generalization performance than the other algorithms.
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Zhang, H., Zhang, S., Yin, Y. (2013). An Improved ELM Algorithm Based on EM-ELM and Ridge Regression. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_95
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DOI: https://doi.org/10.1007/978-3-642-42057-3_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
Online ISBN: 978-3-642-42057-3
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