@inbook{0c104535e21f4c48b9399ac4af95c77e,
title = "A generic sensor fusion problem: Classification and function estimation",
abstract = "A generic fusion problem is studied for multiple sensors whose outputs are probabilistically related to their inputs according to unknown distributions. Sensor measurements are provided as iid input-output samples, and an empirical risk minimization method is described for designing fusers with distribution-free performance bounds. The special cases of isolation and projective fusers for classifiers and function estimators, respectively, are described in terms of performance bounds. The isolation fusers for classifiers are probabilistically guaranteed to perform at least as good as the best classifier. The projective fusers for function estimators are probabilistically guaranteed to perform at least as good as the best subset of estimators.",
author = "Rao, {Nageswara S.V.}",
year = "2004",
doi = "10.1007/978-3-540-25966-4_2",
language = "English",
isbn = "3540221441",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "16--30",
editor = "Fabio Roli and Josef Kittler and Terry Windeatt",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}