Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/7577
Title: Inverse-free extreme learning machine with optimal information updating
Authors: Li, S 
You, ZH
Guo, H
Luo, X
Zhao, ZQ
Keywords: Extreme learning machine (ELM)
Inverse-free
Neural networks
Optimal updates
Issue Date: 1-Jun-2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on cybernetics, 1 June 2015, v. .PP, no.99, pp.1,1 How to cite?
Journal: IEEE transactions on cybernetics 
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.
URI: http://hdl.handle.net/10397/7577
ISSN: 2168-2267
EISSN: 2168-2275
DOI: 10.1109/TCYB.2015.2434841
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.
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
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