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On improving the conditioning of extreme learning machine: A linear case

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conference contribution
posted on 2024-07-09, 17:03 authored by Guopeng Zhao, Zhiqi Shen, Chunyan Miao, Zhihong ManZhihong Man
Recently Extreme Learning Machine (ELM) has been attracting attentions for its simple and fast training algorithm, which randomly selects input weights. Given sufficient hidden neurons, ELM has a comparable performance for a wide range of regression and classification problems. However, in this paper we argue that random input weight selection may lead to an ill-conditioned problem, for which solutions will be numerically unstable. In order to improve the conditioning of ELM, we propose an input weight selection algorithm for an ELM with linear hidden neurons. Experiment results show that by applying the proposed algorithm accuracy is maintained while condition is perfectly stable.

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ISBN

9781424446568

Journal title

2009 7th International Conference on Information, Communications and Signal Processing (ICICS)

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2009 7th International Conference on Information, Communications and Signal Processing ICICS

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IEEE

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Copyright © 2009 IEEE. The published version is reproduced in accordance with the copyright policy of the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

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eng

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