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An efficient extreme learning machine for robust regression
Li DC(李德才); He YQ(何玉庆)
Department机器人学研究室
Conference Name15th International Symposium on Neural Networks, ISNN 2018
Conference DateJune 25-28, 2018
Conference PlaceMinsk, Belarus
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Publication PlaceBerlin
2018
Pages289-299
Indexed ByEI ; CPCI(ISTP)
EI Accession number20182305292389
WOS IDWOS:000460424800013
Contribution Rank1
ISSN0302-9743
ISBN978-3-319-92536-3
KeywordExtreme Learning Machine Robust Regression Bayesian Method Huber Loss Function
Abstract

In this paper, we intend to build a robust extreme learning machine (RELM) with the advantage of both Bayesian framework and Huber loss function. The new method inherits the basic idea of training ELM in a Bayesian framework and replacing the original quadratic loss function by Huber loss function when estimating output weights, in order to enhance the robustness of model. However, the introduction of Huber loss function also yields the prior distribution of model output no longer Gaussian, which makes it difficult to estimate model parameters by using Bayesian method directly. To solve this problem, the iteratively re-weighted least squares (IRWLS) is employed and the Huber cost function can be equivalently transformed into the form of quadratic loss function, which results in an efficient Bayesian method for parameter estimation and remains robust to outliers. We demonstrate with experimental results that the proposed method can effectively increase the robustness of model. © Springer International Publishing AG, part of Springer Nature 2018.

Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/22059
Collection机器人学研究室
AffiliationShenyang Institute of Automation, Chinese Academy of Sciences (CAS), Nanta Street, Shenyang 110016, China
Recommended Citation
GB/T 7714
Li DC,He YQ. An efficient extreme learning machine for robust regression[C]. Berlin:Springer Verlag,2018:289-299.
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