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Abstract:
This paper considers the problem of learning the ranking of a
set of alternatives based upon incomplete information (e.g., a
limited number of observations). We describe two algorithms for
hypothesis ranking and their application for probably approximately
correct (PAC) and expected loss (EL) learning criteria. Empirical
results are provided to demonstrate the effectiveness of these
ranking procedures on both synthetic datasets and real-world data
from a spacecraft design optimization problem.
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