1. An active learning system comprising: a model comprising a probabilistic classifier, which is progressively trained, using labels applied by an annotator to unlabeled elements in a set of elements to be labeled, to associate features of the elements with classes and to predict an appropriate class for an unlabeled element in the set of elements to be labeled, the probabilistic classifier being updated at each of a plurality of iterations to generate a current model with a current probabilistic classifier, whereby at each of the plurality of iterations, the current model assigns to unlabeled elements in the set of elements to be labeled, probabilities that the elements each should be labeled with class labels for a plurality of the classes; a representation module which generates at each of the iterations, a visual representation of a set of the unlabeled elements in the set of elements to be labeled based on the current model, the representation showing the unlabeled elements as data points in a space and showing each of a plurality of classes as a class point in the space, each class point having an associated visual identifier, for each of the iterations, the representation module projecting data points of unlabeled elements to positions in the space which are dependent on the uncertainty of the current model regarding a classification of the respective unlabeled elements, a visual identifier of each of the data points being dependent on the visual identifier of at least two of the class points and the uncertainty of the current model regarding the classification of the element; a graphical user interface which displays the representation to the annotator and enables the annotator to select one of the unlabeled elements shown in the representation for labeling with a class label for one of the classes; an updating module which receives class labels applied by the annotator and updates the model; and a processor which implements the modules.