• Published 2014

Adaptive Transductive Transfer Machines

@inproceedings{Farajidavar2014AdaptiveTT,
  title={Adaptive Transductive Transfer Machines},
  author={Nazli Farajidavar},
  year={2014}
}
Classification methods traditionally work under the assumption that the training and test sets are sampled from similar distributions (domains). However, when such methods are deployed in practise, the conditions in which test data is acquired do not exactly match those of the training set. In this paper, we exploit the fact that it is often possible to gather unlabeled samples from a test/target domain in order to improve the model built from the training source set. We propose Adaptive… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 18 REFERENCES

Generalized Transfer Subspace Learning Through Low-Rank Constraint

Unsupervised Adaptation Across Domain Shifts by Generating Intermediate Data Representations

Geodesic flow kernel for unsupervised domain adaptation

Domain adaptation for object recognition: An unsupervised approach

Transductive transfer learning for action recognition in tennis games

A survey on transfer learning

  • J. T. Kwok, Q. Yang
  • IEEE Transactions on Knowledge and Data Engineering
  • 2010

Bregman Divergence-Based Regularization for Transfer Subspace Learning

Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy