A review of online learning in supervised neural networks

Jain, Lakhmi C., Seera, Manjeevan, Lim, Chee Peng and Balasubramaniam, P. 2014, A review of online learning in supervised neural networks, Neural computing and applications, vol. 25, no. 3-4, pp. 491-509, doi: 10.1007/s00521-013-1534-4.

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Title A review of online learning in supervised neural networks
Author(s) Jain, Lakhmi C.
Seera, Manjeevan
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Balasubramaniam, P.
Journal name Neural computing and applications
Volume number 25
Issue number 3-4
Start page 491
End page 509
Total pages 19
Publisher Springer
Place of publication Berlin, Germany
Publication date 2014-09
ISSN 0941-0643
Keyword(s) Neural networks
Online learning
Supervised learning
Summary Learning in neural networks can broadly be divided into two categories, viz., off-line (or batch) learning and online (or incremental) learning. In this paper, a review of a variety of supervised neural networks with online learning capabilities is presented. Specifically, we focus on articles published in main indexed journals in the past 10 years (2003–2013). We examine a number of key neural network architectures, which include feedforward neural networks, recurrent neural networks, fuzzy neural networks, and other related networks. How the online learning methodologies are incorporated into these networks is exemplified, and how they are applied to solving problems in different domains is highlighted. A summary of the review that covers different network architectures and their applications is presented.
Language eng
DOI 10.1007/s00521-013-1534-4
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2013, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30061698

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