Citation

Tan, Shing Chiang and Lim, Chee Peng and Seera, Manjeeva (2014) An online transfer learning RBF neural network for cross domain data classification. In: Smart Digital Futures 2014. Frontiers in Artificial Intelligence and Applications, 262 . IOS Press, pp. 210-218. ISBN 978-1-61499-405-3

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Abstract

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.

Item Type:	Book Section
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	24 Jul 2014 02:11
Last Modified:	10 Apr 2015 05:03
URII:	http://shdl.mmu.edu.my/id/eprint/5642

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