Multiple instance learning for hidden Markov models: application to landmine detection
Jeremy Bolton, Seniha Esen Yüksel, Paul Gader · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
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.