On Efficient Heuristic Ranking of Hypotheses
Steve Chien, Andre Stechert, Darren Mutz · Neural Information Processing Systems · 1997
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.