Transfer Learning Using the Minimum Description Length Principle with a
  Decision Tree Application
H-böskuldur Hlynsson-A


Abstract:
Transfer learning is about how learning from one domain or a
collection of domains can be applied to another. It is learning from
similarities and parallels, from experience. This paper is about a
distribution free, data driven, extendable framework for transfer
learning, based on the minimum description length principle. We define
transfer learning in terms of a specific framework, where we have a
collection of hypothesis from other application domains available, but
not the data, a learning algorithm consistent over domains and a new,
previously unseen learning task.