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Proceedings Paper

Multiple instance learning for hidden Markov models: application to landmine detection
Author(s): Jeremy Bolton; Seniha Esen Yuksel; Paul Gader
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Paper Abstract

Multiple instance learning is a recently researched learning paradigm in machine intelligence which operates under conditions of uncertainty. A Multiple Instance Hidden Markov Model (MI-HMM) is investigated with applications to landmine detection using ground penetrating radar data. Without introducing any additional parameters, the MI-HMM provides an elegant and simple way to learn the parameters of an HMM in a multiple instance framework. The efficacy of the model is shown on a real landmine dataset. Experiments on the landmine dataset show that MI-HMM learning is effective.

Paper Details

Date Published: 7 June 2013
PDF: 8 pages
Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 87091M (7 June 2013); doi: 10.1117/12.2016489
Show Author Affiliations
Jeremy Bolton, Univ. of Florida (United States)
Seniha Esen Yuksel, Univ. of Florida (United States)
Paul Gader, Univ. of Florida (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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