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Transfer Learning: Methods and Applications

  • Applications: Especially useful when data is outdated or distribution changes, e.g., Web document classification, e-mail spam filtering
  • Instance-based transfer learning: Reweight some of the data from source tasks and use it to learn the target task
  • TrAdaBoost (Transfer AdaBoost)
    • Assume source and target data each described by the same set of attributes (features) & class labels, but rather diff. distributions
    • Require only labeling a small amount of target data
    • Use source data in training: When a source tuple is misclassified, reduce the weight of such tupels so that they will have less effect on the subsequent classifier
  • Research issues
    • Negative transfer: When it performs worse than no transfer at all
    • Heterogeneous transfer learning: Transfer knowledge from different feature space or multiple source domains
    • Large-scale transfer learning


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