Transfer learning using the online FMM model Seera, Manjeevan, Lim, Chee Peng and Loo,Chu Kiong 2014, Transfer learning using the online FMM model. In Loo, Chu Kiong, Yap, Keem Siah, Wong, Kok Wai, Teoh, Andrew and Huang, Kaizhu (ed), Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part I, Springer, Berlin, Germany, pp.151-158, doi: 10.1007/978-3-319-12637-1_19. Attached Files Name Description MIMEType Size Downloads Title Transfer learning using the online FMM model Author(s) Seera, Manjeevan Lim, Chee Peng orcid.org/0000-0003-4191-9083 Loo,Chu Kiong Title of book Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part I Editor(s) Loo, Chu Kiong Yap, Keem Siah Wong, Kok Wai Teoh, Andrew Huang, Kaizhu Publication date 2014 Series Lecture Notes in Computer Science v.8834 Chapter number 19 Total chapters 77 Start page 151 End page 158 Total pages 8 Publisher Springer Place of Publication Berlin, Germany Keyword(s) Data classification Fuzzy min-max neural network Online learning Transfer learning Summary In this paper, we present an analysis on transfer learning using the Fuzzy Min-Max (FMM) neural network with an online learning strategy. Transfer learning leverages information from the source domain in solving problems in the target domain. Using the online FMM model, the data samples are trained one at a time. In order to evaluate the online FMM model, a transfer learning data set, based on data samples collected from real landmines, is used. The experimental results of FMM are analyzed and compared with those from other methods in the literature. The outcomes indicate that the online FMM model is effective for undertaking transfer learning tasks. ISBN 9783319126364 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-319-12637-1_19 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 Persistent URL http://hdl.handle.net/10536/DRO/DU:30069990 Document type: Book Chapter Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 24 Feb 2015, 14:40:15 EST Tue, 24 Feb 2015, 14:42:17 EST Tue, 24 Feb 2015, 14:43:28 EST Wed, 25 Feb 2015, 04:01:32 EST Wed, 25 Feb 2015, 10:04:27 EST Thu, 26 Feb 2015, 05:08:32 EST Mon, 16 Mar 2015, 10:19:38 EST Mon, 16 Mar 2015, 10:24:27 EST Mon, 16 Mar 2015, 12:45:15 EST Mon, 16 Mar 2015, 12:46:41 EST Tue, 17 Mar 2015, 10:35:26 EST Tue, 19 May 2015, 15:00:37 EST Mon, 23 Nov 2015, 09:39:43 EST Sat, 22 Jan 2022, 01:31:57 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 541 Abstract Views, 7 File Downloads  -  Detailed Statistics Created: Tue, 24 Feb 2015, 14:43:28 EST