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This paper addresses the training of classification trees for weakly labelled data. We call “weakly labelled data”, a training set such as the prior labelling information provided refers to vector that indicates the probabilities for instances to belong to each class. Classification tree typically deals with hard labelled data, in this paper a new procedure is suggested in order to train a tree from...
This paper addresses weakly supervised object recognition. We show how the combination of an image-level inference, in terms of image-level object class priors, can lead to better training of object recognition models. Stated within a probabilistic setting, the proposed approach is applied to fisheries acoustics and fish school recognition.
Statistical training allows the establishment of a probabilistic classification model. In the supervised case, the model is assessed from a labelled dataset, i.e. each observed data has a label. In the weakly-supervised case, the label is not exactly known. In our instance, the probability to associate the observation to the different classes is known. Thus, labels for the data are a probability vector...
This paper addresses the inference of probabilistic classification models using weakly supervised learning. In contrast to previous work, the use of proportion-based training data is investigated in combination to non-linear classification models. An application to fisheries acoustics and fish school classification is considered and experiments are reported for synthetic and real datasets.
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