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.
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Editors: Hans Ulrich Simon, Gabor Lugosi, Avrim Blum.
A preliminary version of this work was presented in the 19th Annual Conference on Learning Theory (COLT), 2006.
This work was supported in part by the IST Programme of the European Community, under the PASCAL Network of Excellence, IST-2002-506778, by a grant No. 1079/04 from the Israel Science Foundation, by a grant from BSF and an IBM faculty award. This publication only reflects the authors’ views.
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Fine, S., Mansour, Y. Active sampling for multiple output identification. Mach Learn 69, 213–228 (2007). https://doi.org/10.1007/s10994-007-5026-6
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DOI: https://doi.org/10.1007/s10994-007-5026-6