On Efficient Heuristic Ranking of Hypotheses

Part of Advances in Neural Information Processing Systems 10 (NIPS 1997)

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Steve Chien, Andre Stechert, Darren Mutz


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 hypoth(cid:173) esis ranking and their application for probably approximately cor(cid:173) rect (PAC) and expected loss (EL) learning criteria. Empirical results are provided to demonstrate the effectiveness of these rank(cid:173) ing procedures on both synthetic datasets and real-world data from a spacecraft design optimization problem.