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A novel Multiple Instance Hidden Markov Model (MI-HMM) is introduced for classification of ambiguous time-series data, and its training is accomplished via Metropolis-Hastings sampling. 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 Learning (MIL) framework. The efficacy of the model is shown on a real landmine dataset. Experiments on the landmine dataset show that MI-HMM learning is verydoi:10.1109/mlsp.2012.6349734 dblp:conf/mlsp/YukselBG12 fatcat:5j3mq3cjrrawfo4c3vsxu5bbvy