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Iterative extreme learning machine
Author(s)
Jiramaneepinit, Boonnithi
Watchareeruetai, Ukrit
Date Issued
July 2, 2018
Type
Conference Paper
Abstract
This paper proposes a simple but effective method to improve the generalization performance of extreme learning machine (ELM), which is an extremely fast learning method for a single-hidden-layer feedforward neural network (SLFN). The proposed method adopts an online sequential learning technique to update the output weight matrix of a learned SLFN by using misclassified training samples. As the process of updating these weights could be iteratively performed, the proposed method is named iterative ELM (I-ELM). The proposed I-ELM was evaluated on three datasets, including MNIST, Small NORB, and CIFAR-10, and compared with the standard ELM. Experimental results indicate that by using only a few iterations, the proposed I-ELM could effectively improve the generalization performance of SLFNs.
Citation
2018 22nd International Computer Science and Engineering Conference Icsec 2018, 2018