Machine Learning

, Volume 69, Issue 2–3, pp 213–228

Active sampling for multiple output identification



We study functions with multiple output values, and use active sampling to identify an example for each of the possible output values. Our results for this setting include: (1) Efficient active sampling algorithms for simple geometric concepts, such as intervals on a line and axis parallel boxes. (2) A characterization for the case of binary output value in a transductive setting. (3) An analysis of active sampling with uniform distribution in the plane. (4) An efficient algorithm for the Boolean hypercube when each output value is a monomial.


Active learning Active sampling Hitting VC dimension Transductive learning Output identification Separation dimension 


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.IBM Research Laboratory in HaifaMount Carmel, HaifaIsrael
  2. 2.School of Computer ScienceTel Aviv UniversityTel AvivIsrael

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