An online transfer learning RBF neural network for cross domain data classification

Tan, SC, Lim, Chee Peng and Seera, M 2014, An online transfer learning RBF neural network for cross domain data classification. In Neves-Silva, R, Tshirintzis, GA, Uskov, V, Howlett, RJ and Jain, LC (ed), Volume 262: Smart Digital Futures 2014, IOS Press, Amsterdam, The Netherlands, pp.210-218, doi: 10.3233/978-1-61499-405-3-210.

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Title An online transfer learning RBF neural network for cross domain data classification
Author(s) Tan, SC
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Seera, M
Title of book Volume 262: Smart Digital Futures 2014
Editor(s) Neves-Silva, R
Tshirintzis, GA
Uskov, V
Howlett, RJ
Jain, LC
Publication date 2014
Chapter number 22
Total chapters 79
Start page 210
End page 218
Total pages 9
Publisher IOS Press
Place of Publication Amsterdam, The Netherlands
Keyword(s) classification
online learning
radial basis function network
Transfer learning
Summary In this paper, a Radial Basis Function Network (RBFN) trained with the Dynamic Decay Adjustment (DDA) algorithm (i.e., RBFNDDA) is deployed as an incremental learning model for tackling transfer learning problems. An online learning strategy is exploited to allow the RBFNDDA model to transfer knowledge from one domain and applied to classification tasks in a different yet related domain. An experimental study is carried out to evaluate the effectiveness of the online RBFNDDA model using a benchmark data set obtained from a public domain. The results are analyzed and compared with those from other methods. The outcomes positively reveal the potentials of the online RBFNDDA model in handling transfer learning tasks.
ISBN 9781614994046
ISSN 0922-6389
Language eng
DOI 10.3233/978-1-61499-405-3-210
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2014, IOS Press
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070585

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