On Efficient Heuristic Ranking of Hypotheses
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 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.
Cite
Text
Chien et al. "On Efficient Heuristic Ranking of Hypotheses." Neural Information Processing Systems, 1997.Markdown
[Chien et al. "On Efficient Heuristic Ranking of Hypotheses." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/chien1997neurips-efficient/)BibTeX
@inproceedings{chien1997neurips-efficient,
title = {{On Efficient Heuristic Ranking of Hypotheses}},
author = {Chien, Steve A. and Stechert, Andre and Mutz, Darren},
booktitle = {Neural Information Processing Systems},
year = {1997},
pages = {444-450},
url = {https://mlanthology.org/neurips/1997/chien1997neurips-efficient/}
}