Bayesian Active Transfer Learning in Smart Homes
Tom Diethe, Niall Twomey, Peter A. Flach · 2015
There are at least two major challenges for machine learning in the smart-home setting. Firstly, the deployment context will be very different to the the context in which learning occurs, due to both individual differences in typical activity patterns anddifferent houseand sensor layouts. Secondly, accurate labelling of training data is an extremely time-consuming process, and the resulting labels are potentially noisy and error-prone. The resulting framework is therefore a combination of active and transfer learning. We argue that hierarchical Bayesian methods are particularly well suited to problems of this nature, and give a possible formulation of such a model.