Journal
NEURAL COMPUTING & APPLICATIONS
Volume 25, Issue 2, Pages 469-480Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-013-1517-5
Keywords
Transfer learning; Online Fuzzy Min-Max neural network; Noisy data; Data classification
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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.
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