Extreme learning machine for regression and multiclass classification

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification application...

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Main Authors: Huang, Guang-Bin, Zhou, Hongming, Ding, Xiaojian, Zhang, Rui
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2013
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Online Access:https://hdl.handle.net/10356/96388
http://hdl.handle.net/10220/11436
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-963882020-03-07T14:02:46Z Extreme learning machine for regression and multiclass classification Huang, Guang-Bin Zhou, Hongming Ding, Xiaojian Zhang, Rui School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the “generalized” single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM. 2013-07-15T07:07:51Z 2019-12-06T19:29:46Z 2013-07-15T07:07:51Z 2019-12-06T19:29:46Z 2011 2011 Journal Article Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2012). Extreme Learning Machine for Regression and Multiclass Classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2), 513-529. 1083-4419 https://hdl.handle.net/10356/96388 http://hdl.handle.net/10220/11436 10.1109/TSMCB.2011.2168604 en IEEE transactions on systems, man, and cybernetics, part b (cybernetics) © 2011 IEEE.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Huang, Guang-Bin
Zhou, Hongming
Ding, Xiaojian
Zhang, Rui
Extreme learning machine for regression and multiclass classification
description Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the “generalized” single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Huang, Guang-Bin
Zhou, Hongming
Ding, Xiaojian
Zhang, Rui
format Article
author Huang, Guang-Bin
Zhou, Hongming
Ding, Xiaojian
Zhang, Rui
author_sort Huang, Guang-Bin
title Extreme learning machine for regression and multiclass classification
title_short Extreme learning machine for regression and multiclass classification
title_full Extreme learning machine for regression and multiclass classification
title_fullStr Extreme learning machine for regression and multiclass classification
title_full_unstemmed Extreme learning machine for regression and multiclass classification
title_sort extreme learning machine for regression and multiclass classification
publishDate 2013
url https://hdl.handle.net/10356/96388
http://hdl.handle.net/10220/11436
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