Flexible Transfer Learning under Support and Model Shift

@inproceedings{Wang2014FlexibleTL,
  title={Flexible Transfer Learning under Support and Model Shift},
  author={Xuezhi Wang and Jeff G. Schneider},
  booktitle={NIPS},
  year={2014}
}
In a classical transfer learning setting, we have sufficient fully labeled data from the source domain (or the training domain) where we fully observe the data points X, and all corresponding labels Y tr are known. On the other hand, we are given data points, X, from the target domain (or the test domain), but few or none of the corresponding labels, Y , are given. The source and the target domains are related but not identical, thus the joint distributions, P (X, Y ) and P (X, Y ), are… CONTINUE READING
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