Ground Penetrating Radar (GPR) data provides a powerful technique to identify subsurface buried threats.
Although GPR data contains a three-dimensional representation of the subsurface, object truth (i.e. labels and
positions of true threat objects in training lanes) is often provided in only two dimensions (GPS coordinates
along the earth's surface). To mitigate uncertainty in an object's location in depth, many successful feature extraction/
object recognition techniques in GPR extract feature vectors from several depth regions, and attempt
to combine information across these feature vectors to make final decisions. However, many machine learning
techniques are not well suited for learning under these conditions. Multiple Instance Learning (MIL) is a type of
supervised learning method in which labels are available for sets of samples, but not for individual samples. The
goal of learning in MIL is to classify new sets of samples as they become available. This set-based framework
is useful in processing GPR responses since features are often extracted independently from multiple un-labeled
depth bins, and thus a set of features is produced at each potential threat location. In this work, a comparison
of several previous approaches to MlL applied to landmine detection in GPR data is presented. One recent
algorithm, the p-Posterior Mixture Model approach (pPMM) is given special attention, and several slight modifications
to the pPMM approach are presented and compared.