Ensemble Transfer Learning for IoT — Present and Future Research

  • Transfer Learning in various IoT applications using sensor fusion technology
    Motivation
    Deep learning has been known to learn non-linear data representations for large and complex datasets.
  • Transfer Learning across Domains on different time-periods Source: https://www.slideshare.net/jins0618/transfer-learning-an-overview
    Transfer Learning have been known to solve problems by Offering a set of algorithms that identify similar areas of knowledge which are “transferable” to the target domain.
  • Combining algorithms and models from a set of similar domains to the target domain, a concept known as ensemble transfer learning.
  • Transfer knowledge across domains or tasks from numerous source domains that have different feature spaces, a concept known as heterogeneous transfer learning.
  • Transfer knowledge between entirely different feature spaces, a concept known as translated learning.
  • TrResampling, works on multiple iterations which generates new training data set at each step, by combining weighted-resamples from the original source data set along with the labeled data in the target data set.

Read full article: towardsdatascience.com

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