Uncertainty heuristics of large margin active learning for hyperspectral image classification

The difficulties of having expertise in expert systems, the increasing of the data volume, self adaptation and prediction, all those problems are solved in the presence of learning. The classical definition of learning in cognitive science is the ability to improve the performance as the exercise of an activity. With learning, knowledge is automatically extracted from a data set. In this paper, we are interested to study efficient active learning methods. These methods are based on the definition of an efficient training set by iteratively adapting it through adding the most informative unlabeled instances. The selection of these instances are generally based on an uncertainty and diversity criteria. This study is focused on the uncertainty criterion. A review of the principal families of active learning algorithms is presented. Then the large-margin active learning techniques are detailed and evaluations of the contribution of large margin uncertainty criteria are presented.