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A review of online learning in supervised neural networks

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dc.contributor.authorJain, L.C.
dc.contributor.authorSeera, M.
dc.contributor.authorLim, C.P.
dc.contributor.authorBalasubramaniam, P.
dc.date.issued2014
dc.description.abstractLearning 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.
dc.identifier.citationNeural Computing and Applications, 2014; 25(3):491-509
dc.identifier.doi10.1007/s00521-013-1534-4
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.urihttps://hdl.handle.net/1959.8/160207
dc.language.isoen
dc.publisherSpringer
dc.rightsCopyright 2013 Springer-Verlag London
dc.source.urihttps://doi.org/10.1007/s00521-013-1534-4
dc.subjectneural networks
dc.subjectonline learning
dc.subjectsupervised learning
dc.titleA review of online learning in supervised neural networks
dc.typeJournal article
pubs.publication-statusPublished
ror.mmsid9915910168401831

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