Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/120140
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Type: Conference paper
Title: Regularized training of the extreme learning machine using the conjugate gradient method
Author: De Chazal, P.
McDonnell, M.
Citation: 2017 International Joint Conference on Neural Networks (IJCNN), 2017 / vol.2017-May, pp.1802-1808
Publisher: IEEE
Issue Date: 2017
Series/Report no.: IEEE International Joint Conference on Neural Networks (IJCNN)
ISBN: 9781509061822
ISSN: 2161-4393
2161-4407
Conference Name: International Joint Conference on Neural Networks (IJCNN) (14 May 2017 - 19 May 2017 : Anchorage, AK)
Statement of
Responsibility: 
Philip de Chazal, Mark D. McDonnell
Abstract: We describe a new algorithm providing regularized training of the extreme learning machine (ELM) that uses a modified conjugate gradient (CG) method to determine the network hidden to output weights. The CG method is modified to include a validation set performance calculation at each iteration step. The solution is initialized to zero and during the CG iterations, we monitor the validation set error. When the error begins to rise we terminate the CG algorithm. The operations per iteration is O(P2), where P is the number of output weights, which is significantly faster than the O(P3) operations per iteration required by ridge regression regularization methods. We demonstrate the effectiveness of our method by classifying the MNIST database and achieve an accuracy of 99.2% using an ELM classifier processing the unmodified pixel values.
Keywords: Cholesky; conjugate gradient method; early stopping; Extreme Learning Machine; regularization; QR; SVD
Rights: © 2017 Crown
RMID: 0030076860
DOI: 10.1109/IJCNN.2017.7966069
Grant ID: http://purl.org/au-research/grants/arc/FT110101098
Appears in Collections:Electrical and Electronic Engineering publications

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