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Found 2 papers, 1 papers with code
Learn on Source, Refine on Target:A Model Transfer Learning Framework with Random Forests
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.
The Perturbed Variation
We introduce a new discrepancy score between two distributions that gives an indication on their \emph{similarity}.
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