Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96388
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dc.contributor.authorHuang, Guang-Binen
dc.contributor.authorZhou, Hongmingen
dc.contributor.authorDing, Xiaojianen
dc.contributor.authorZhang, Ruien
dc.date.accessioned2013-07-15T07:07:51Zen
dc.date.accessioned2019-12-06T19:29:46Z-
dc.date.available2013-07-15T07:07:51Zen
dc.date.available2019-12-06T19:29:46Z-
dc.date.copyright2011en
dc.date.issued2011en
dc.identifier.citationHuang, 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.en
dc.identifier.issn1083-4419en
dc.identifier.urihttps://hdl.handle.net/10356/96388-
dc.identifier.urihttp://hdl.handle.net/10220/11436en
dc.description.abstractDue 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.en
dc.language.isoenen
dc.relation.ispartofseriesIEEE transactions on systems, man, and cybernetics, part b (cybernetics)en
dc.rights© 2011 IEEE.en
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleExtreme learning machine for regression and multiclass classificationen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.identifier.doihttp://dx.doi.org/10.1109/TSMCB.2011.2168604en
item.grantfulltextnone-
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