Generalized extreme learning machine acting on a metric space

Abstract

Generalized Extreme Learning Machine (GELM) is a kind of fast and efficient learning algorithm for training Generalized Single-layer hidden Feedforward Networks (GSLFNs) acting on some metric spaces. However, noisy data often produce over-fitting phenomena in practical applications. Therefore, an improved learning algorithm, called Regularized Generalized Extreme Learning Machine (R-GELM), is proposed with regularization method to improve the generalization of GELM. Experimental comparisons of the proposed R-GELM are carried out with four state-of-the-art algorithms, and the experimental results show that the proposed R-GELM has better generalization and less computational cost than the others.

DOI: 10.1007/s00500-012-0825-5

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Cite this paper

@article{Zhao2012GeneralizedEL, title={Generalized extreme learning machine acting on a metric space}, author={Jianwei Zhao and Dong Sun Park and Joonwhoan Lee and Feilong Cao}, journal={Soft Comput.}, year={2012}, volume={16}, pages={1503-1514} }