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Proceedings Paper

Uncertainty characterization using copulas for classification
Author(s): Onur Ozdemir; Sora Choi; Thomas G. Allen; Pramod K. Varshney
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Paper Abstract

We address the problem of characterizing uncertainty for multisensor data fusion in a classification problem. To achieve this goal, we model the joint density of given multivariate data using copula functions while allowing the ability to incorporate any desired marginal distributions, i.e., any desired modalities. The proposed model is data driven in that the corresponding copula functions and their parameters are learned from the data. Our results show that the proposed framework can capture the uncertainties more accurately than current state of the practice, and lead to robust and improved classification performance compared to traditional classifiers.

Paper Details

Date Published: 22 May 2015
PDF: 15 pages
Proc. SPIE 9498, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2015, 94980A (22 May 2015); doi: 10.1117/12.2181908
Show Author Affiliations
Onur Ozdemir, Boston Fusion Corp. (United States)
Sora Choi, Syracuse Univ. (United States)
Thomas G. Allen, Boston Fusion Corp. (United States)
Pramod K. Varshney, Syracuse Univ. (United States)

Published in SPIE Proceedings Vol. 9498:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2015
Jerome J. Braun, Editor(s)

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