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.

Cited by: 33|Bibtex|Views13|DOI:https://doi.org/10.1109/TPAMI.2016.2618118
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Other Links: academic.microsoft.com|dblp.uni-trier.de|pubmed.ncbi.nlm.nih.gov|arxiv.org

Abstract:

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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