Machine Learning

, Volume 69, Issue 2, pp 213–228

Active sampling for multiple output identification

Authors

    • IBM Research Laboratory in Haifa
  • Yishay Mansour
    • School of Computer ScienceTel Aviv University
Article

DOI: 10.1007/s10994-007-5026-6

Cite this article as:
Fine, S. & Mansour, Y. Mach Learn (2007) 69: 213. doi:10.1007/s10994-007-5026-6
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Abstract

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.

Keywords

Active learningActive samplingHittingVC dimensionTransductive learningOutput identificationSeparation dimension
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Copyright information

© Springer Science+Business Media, LLC 2007