Transfer learning using the online fuzzy min–max neural network

Manjeevan Seera, Chee Peng Lim

Research output: Contribution to journalArticleResearchpeer-review

12 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)469-480
Number of pages12
JournalNeural Computing and Applications
Volume25
Issue number2
DOIs
Publication statusPublished - Aug 2014
Externally publishedYes

Keywords

  • Data classification
  • Noisy data
  • Online Fuzzy min–max neural network
  • Transfer learning

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