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An online transfer learning RBF neural network for cross domain data classification
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.
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Title of book
Volume 262: Smart Digital Futures 2014Volume
262Chapter number
22Pagination
210 - 218Publisher
IOS PressPlace of publication
Amsterdam, The NetherlandsPublisher DOI
ISSN
0922-6389ISBN-13
9781614994046Language
engPublication classification
B Book chapter; B1 Book chapterCopyright notice
2014, IOS PressExtent
79Editor/Contributor(s)
R Neves-Silva, G Tshirintzis, V Uskov, R Howlett, L JainUsage metrics
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