Active transfer learning for activity recognition

Tom Diethe, Niall Twomey, Peter Flach

Research output: Contribution to conferenceConference Paperpeer-review

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We examine activity recognition from accelerometers, which provides at least two major challenges for machine learning. Firstly, the deployment context is likely to differ from the learning context. Secondly, accurate labelling of training data is time-consuming and error-prone. This calls for a combination of active and transfer learning. We derive a hierarchical Bayesian model that is a natural fit to such problems, and provide empirical validation on synthetic and publicly available datasets. The results show that by combining active and transfer learning, we can achieve faster learning with fewer labels on a target domain than by either alone.
Original languageEnglish
Number of pages6
Publication statusPublished - 2016
EventEuropean Symposium on Artificial Neural Networks - Belgium, Bruges, United Kingdom
Duration: 27 Apr 201629 Apr 2016


ConferenceEuropean Symposium on Artificial Neural Networks
Country/TerritoryUnited Kingdom

Structured keywords

  • Jean Golding


  • active learning
  • transfer learning
  • smart home
  • accelerometers


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