A Review of Online Learning in Supervised Neural Networks

Lakhmi JAIN, Manjeevan Seera, Chee Peng Lim, P. Balasubramaniam

Research output: Contribution to journalReview articlepeer-review

84 Citations (Scopus)


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 feed forward 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
Original languageEnglish
Pages (from-to)491-509
Number of pages19
JournalNeural Computing and Applications
Issue number3-4
Publication statusPublished - 2014
Externally publishedYes


Dive into the research topics of 'A Review of Online Learning in Supervised Neural Networks'. Together they form a unique fingerprint.

Cite this