Querying discriminative and representative samples for batch mode active learning

  title={Querying discriminative and representative samples for batch mode active learning},
  author={Zheng Wang and Jieping Ye},
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
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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
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