Querying discriminative and representative samples for batch mode active learning

@inproceedings{Wang2013QueryingDA,
  title={Querying discriminative and representative samples for batch mode active learning},
  author={Zheng Wang and Jieping Ye},
  booktitle={KDD},
  year={2013}
}
Empirical risk minimization (ERM) provides a useful guideline for many machine learning and data mining algorithms. Under the ERM principle, one minimizes an upper bound of the true risk, which is approximated by the summation of empirical risk and the complexity of the candidate classifier class. To guarantee a satisfactory learning performance, ERM requires that the training data are i.i.d. sampled from the unknown source distribution. However, this may not be the case in active learning… CONTINUE READING
Highly Cited
This paper has 92 citations. REVIEW CITATIONS
37 Citations
5 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 37 extracted citations

92 Citations

0204020142015201620172018
Citations per Year
Semantic Scholar estimates that this publication has 92 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-5 of 5 references

Distributed optimization and statistical learning via the alternating direction method of multipliers

  • Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein.
  • Foundations and Trends in Machine Learning 3, 1…
  • 2011
Highly Influential
4 Excerpts

Similar Papers

Loading similar papers…