Learn on Source, Refine on Target:A Model Transfer Learning Framework with Random Forests

2 code implementations • 4 Nov 2015 • Noam Segev, Maayan Harel, Shie Mannor, Koby Crammer, Ran El-Yaniv

We propose novel model transfer-learning methods that refine a decision forest model M learned within a "source" domain using a training set sampled from a "target" domain, assumed to be a variation of the source.

Transfer Learning