Learn on Source, Refine on Target:A Model Transfer Learning Framework with Random Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1811-1824, 2017.
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. We present two random forest transfer algorithms. The first algorithm searches greedily for locally optimal modifications of e...More
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