Transfer learning using the online Fuzzy Min-Max neural network

Seera, Manjeevan and Lim, Chee Peng 2014, Transfer learning using the online Fuzzy Min-Max neural network, Neural computing and applications, vol. 25, no. 2, pp. 469-480, doi: 10.1007/s00521-013-1517-5.

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Title Transfer learning using the online Fuzzy Min-Max neural network
Author(s) Seera, Manjeevan
Lim, Chee PengORCID iD for Lim, Chee Peng
Journal name Neural computing and applications
Volume number 25
Issue number 2
Start page 469
End page 480
Total pages 12
Publisher Springer
Place of publication Berlin, Germany
Publication date 2014-08
ISSN 0941-0643
Keyword(s) Data classification
Noisy data
Online Fuzzy Min-Max neural network
Transfer learning
Summary In this paper, we present an empirical analysis on transfer learning using the Fuzzy Min–Max (FMM) neural network with an online learning strategy. Three transfer learning benchmark data sets, i.e., 20 Newsgroups, WiFi Time, and Botswana, are used for evaluation. In addition, the data samples are corrupted with white Gaussian noise up to 50 %, in order to assess the robustness of the online FMM network in handling noisy transfer learning tasks. The results are analyzed and compared with those from other methods. The outcomes indicate that the online FMM network is effective for undertaking transfer learning tasks in noisy environments.
Language eng
DOI 10.1007/s00521-013-1517-5
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
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Created: Tue, 18 Mar 2014, 08:41:15 EST

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