A review of online learning in supervised neural networks
Date
2014
Authors
Jain, L.C.
Seera, M.
Lim, C.P.
Balasubramaniam, P.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Neural Computing and Applications, 2014; 25(3):491-509
Statement of Responsibility
Conference Name
Abstract
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.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2013 Springer-Verlag London