Dissimilarity-Based Multiple Instance Learning

Multiple instance learning (MIL) is an extension of supervised learning where the objects are represented by sets (bags) of feature vectors (instances) rather than individual feature vectors. For example, an image can be represented by a bag of instances, where each instance is a patch in that image. Only bag labels are given, however, the...