7 June 2013 Multiple instance learning for hidden Markov models: application to landmine detection
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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.
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Jeremy Bolton, Jeremy Bolton, Seniha Esen Yuksel, Seniha Esen Yuksel, Paul Gader, Paul Gader, "Multiple instance learning for hidden Markov models: application to landmine detection", Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 87091M (7 June 2013); doi: 10.1117/12.2016489; https://doi.org/10.1117/12.2016489
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