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The Edge Histogram Detector (EHD) is a well-researched and tested algorithm that has been integrated into fielded systems for landmine detection using Ground Penetrating Radar (GPR) sensor. It uses edge histogram based features and a possibilistic K-Nearest Neighbors (KNN) classifier. Due to the inherent static data representation and static classifier architecture, the EHD may not be very effective in detecting targets with large variations in shape and size. In this paper, we propose a more flexible approach that is based on multiple instance learning. First, we summarize the training data and identify representative mine alarms. This summarization step could be achieved using one or multiple feature representation to capture different characteristics of the data. The identified prototypes, also called bags of mines, will be used to map the alarms to a feature space that improves the discrimination between mines and clutter objects. The second step of our approach consists of building a classifier on the mapped feature space. We experiment with two different classifiers. The first one is a simple linear classifier that compares the features in the mapped space. The second classifier is based on learning Relevance Vector Machines (RVM) in the sparse mapped space. Our initial experiments on large and diverse Ground Penetrating Radar data collections show that the proposed approach can outperform the baseline EHD.