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Multiple-instance learning (MIL) is a learning paradigm used for learning a target concept in the presence of noise or with an uncertainty in target information including class labels. Due to the difficult situations in which hyperspectral images (HSIs) are collected, research in this area is extremely relevant and directly applicable. In the following, an MIL framework is proposed for target spectra learning for HSI analysis. MIL techniques are compared to their non-MIL counterparts (standard machine learning techniques). Experimental results indicate that MIL can learn target spectra with a lack of target information and, furthermore, result in improved classifiers.