Bayesian Active Transfer Learning

Code accompanying the paper:

@inproceedings{diethe2016active,
	Author = {Tom Diethe and Niall Twomey and Peter Flach},
	Booktitle = {24th European Symposium on Artificial Neural Networks, {ESANN} 2016, Bruges, Belgium, April 27-29, 2016},
	Title = {Active transfer learning for activity recognition},
	Year = {2016}
	}

Tom Diethe and Niall Twomey and Peter Flach

Intelligent Systems Laboratory, University of Bristol, UK

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 and different house and 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