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Proceedings Paper

Embedding the multiple instance problem: applications to landmine detection with ground penetrating radar
Author(s): Jeremy Bolton; Paul Gader; Hichem Frigui
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Paper Abstract

Multiple Instance Learning is a recently researched learning paradigm in machine intelligence which operates under conditions of uncertainty with the cost of increased computational burden. This increase in computational burden can be avoided by embedding these so-called multiple instances using a kernel function or other embedding function. In the following, a family of fast multiple instance relevance vector machines are used to learn and classify landmine signatures in ground penetrating radar data. Results indicate a significant reduction in computational complexity without a loss in classification accuracy in operating conditions.

Paper Details

Date Published: 7 June 2013
PDF: 6 pages
Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 87091Q (7 June 2013); doi: 10.1117/12.2019027
Show Author Affiliations
Jeremy Bolton, Univ. of Florida (United States)
Paul Gader, Univ. of Florida (United States)
Hichem Frigui, Univ. of Louisville (United States)

Published in SPIE Proceedings Vol. 8709:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII
J. Thomas Broach; Jason C. Isaacs, Editor(s)

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