Transfer learning using the online FMM model
Contribuinte(s) |
Loo, Chu Kiong Yap, Keem Siah Wong, Kok Wai Teoh, Andrew Huang, Kaizhu |
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Data(s) |
01/01/2014
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Resumo |
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. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Springer |
Relação |
http://dro.deakin.edu.au/eserv/DU:30069990/seera-transferlearning-2014.pdf http://dro.deakin.edu.au/eserv/DU:30069990/seera-transferlearning-evid-2014.pdf http://doi.org/10.1007/978-3-319-12637-1_19 |
Palavras-Chave | #Data classification #Fuzzy min-max neural network #Online learning #Transfer learning |
Tipo |
Book Chapter |