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Transfer learning using the online Fuzzy Min–Max neural network

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Abstract

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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Authors and Affiliations

  1. Intel Technology, Bayan Lepas, Penang, Malaysia

    Manjeevan Seera

  2. Deakin University, Geelong, VIC, Australia

    Chee Peng Lim

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  1. Manjeevan Seera
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  2. Chee Peng Lim
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Correspondence to Chee Peng Lim.

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Seera, M., Lim, C.P. Transfer learning using the online Fuzzy Min–Max neural network. Neural Comput & Applic 25, 469–480 (2014). https://doi.org/10.1007/s00521-013-1517-5

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  • DOI: https://doi.org/10.1007/s00521-013-1517-5

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