Representing and Reasoning with Defaults for Learning Agents
Benjamin N. Grosof
AAAI-SS 1993
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. © 2007 Springer Science+Business Media, LLC.
Benjamin N. Grosof
AAAI-SS 1993
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