Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/7577
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Li, S | - |
dc.creator | You, ZH | - |
dc.creator | Guo, H | - |
dc.creator | Luo, X | - |
dc.creator | Zhao, ZQ | - |
dc.date.accessioned | 2015-11-10T08:32:57Z | - |
dc.date.available | 2015-11-10T08:32:57Z | - |
dc.identifier.issn | 2168-2267 | - |
dc.identifier.uri | http://hdl.handle.net/10397/7577 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Li, S.; You, Z.-H.; Guo, H.; Luo, X.; Zhao, Z.-Q., "Inverse-Free Extreme Learning Machine With Optimal Information Updating," IEEE Transactions on Cybernetics, vol.PP, no.99, pp.1,1 is available at http://dx.doi.org/10.1109/TCYB.2015.2434841 | en_US |
dc.subject | Extreme learning machine (ELM) | en_US |
dc.subject | Inverse-free | en_US |
dc.subject | Neural networks | en_US |
dc.subject | Optimal updates | en_US |
dc.title | Inverse-free extreme learning machine with optimal information updating | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.issue | 99 | - |
dc.identifier.doi | 10.1109/TCYB.2015.2434841 | - |
dcterms.abstract | The extreme learning machine (ELM) has drawn insensitive research attentions due to its effectiveness in solving many machine learning problems. However, the matrix inversion operation involved in the algorithm is computational prohibitive and limits the wide applications of ELM in many scenarios. To overcome this problem, in this paper, we propose an inverse-free ELM to incrementally increase the number of hidden nodes, and update the connection weights progressively and optimally. Theoretical analysis proves the monotonic decrease of the training error with the proposed updating procedure and also proves the optimality in every updating step. Extensive numerical experiments show the effectiveness and accuracy of the proposed algorithm. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on cybernetics, 1 June 2015, v. .PP, no.99, pp.1,1 | - |
dcterms.isPartOf | IEEE transactions on cybernetics | - |
dcterms.issued | 2015-06-01 | - |
dc.identifier.eissn | 2168-2275 | - |
dc.identifier.rosgroupid | 2014002571 | - |
dc.description.ros | 2014-2015 > Academic research: refereed > Publication in refereed journal | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Li_Inverse-free_Extreme_Learning.pdf | 1.34 MB | Adobe PDF | View/Open |
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