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A review of online learning in supervised neural networks

journal contribution
posted on 2014-09-01, 00:00 authored by L Jain, M Seera, Chee Peng LimChee Peng Lim, P Balasubramaniam
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.

History

Journal

Neural computing and applications

Volume

25

Issue

3-4

Pagination

491 - 509

Publisher

Springer

Location

Berlin, Germany

ISSN

0941-0643

eISSN

1433-3058

Language

eng

Publication classification

C1 Refereed article in a scholarly journal; C Journal article

Copyright notice

2013, Springer