Transfer learning using the online FMM model


Autoria(s): Seera, Manjeevan; Lim, Chee Peng; Loo,Chu Kiong
Contribuinte(s)

Loo, Chu Kiong

Yap, Keem Siah

Wong, Kok Wai

Teoh, Andrew

Huang, Kaizhu

Data(s)

01/01/2014

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

http://hdl.handle.net/10536/DRO/DU:30069990

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