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Multiple Instance Learning (MIL) proposes a new paradigm when instance labeling, in the learning step, is not possible or infeasible, by assigning a single label (positive or negative) to a set of instances called bag. In this paper, an operator based on homogeneity of positive bags for MIL is introduced. Our method consists in removing instances from the positives bags according to their simil...

We propose a multiple instance learning approach to contentbased retrieval of classroom video for the purpose of supporting human assessing the learning environment. The key element of our approach is a mapping between the semantic concepts of the assessment system and features of the video that can be measured using techniques from the fields of computer vision and speech analysis. We report o...

Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In this setting, supervised learning cannot be applied directly. Often, specialized MIL methods learn by making additional assumptions about the relationship of the bag labels and instance labels. Such assumptions may fit a particular dataset, ...