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Proceedings Paper

To fuse or not to fuse: fuser versus best classifier
Author(s): Nageswara S. V. Rao
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Paper Abstract

A sample from a class defined on a finite-dimensional Euclidean space and distributed according to an unknown distribution is given. We are given a set of classifiers each of which chooses a hypothesis with least misclassification error from a family of hypotheses. We address the question of choosing the classifier with the best performance guarantee versus combining the classifiers using a fuser. We first describe a fusion method based on isolation property such that the performance guarantee of the fused system is at least as good as the best of the classifiers. For a more restricted case of deterministic classes, we present a method based on error set estimation such that the performance guarantee of fusing all classifiers is at least as good as that of fusing any subset of classifiers.

Paper Details

Date Published: 20 March 1998
PDF: 10 pages
Proc. SPIE 3376, Sensor Fusion: Architectures, Algorithms, and Applications II, (20 March 1998); doi: 10.1117/12.303685
Show Author Affiliations
Nageswara S. V. Rao, Oak Ridge National Lab. (United States)


Published in SPIE Proceedings Vol. 3376:
Sensor Fusion: Architectures, Algorithms, and Applications II
Belur V. Dasarathy, Editor(s)

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