Active Sampling for Multiple Output Identification

  • Shai Fine
  • Yishay Mansour
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4005)


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 Function Class Target Function Active Sample Separation Dimension 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shai Fine
    • 1
  • Yishay Mansour
    • 2
  1. 1.IBM Research Laboratory in HaifaIsrael
  2. 2.School of Computer ScienceTel Aviv UniversityTel AvivIsrael

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