Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels

@inproceedings{Zhang2013CovariateSI,
  title={Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels},
  author={Kai Zhang and Vincent Wenchen Zheng and Qiaojun Wang and James T. Kwok and Qiang Yang and Ivan Marsic},
  booktitle={ICML},
  year={2013}
}
Covariate shift is an unconventional learning scenario in which training and testing data have different distributions. A general principle to solve the problem is to make the training data distribution similar to that of the test domain, such that classifiers computed on the former generalize well to the latter. Current approaches typically target on sample distributions in the input space, however, for kernel-based learning methods, the algorithm performance depends directly on the geometry… CONTINUE READING
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