In many situations large datasets are available but unfortunately labeling is expensive and time consuming. Active Learning is a concept for building classifiers by letting the algorithm choose the training data it uses. This achieves greater accuracy than just labeling a random subset of the available dataset.
The active learning algorithm selects some unlabeled data instances which are then labeled by a human annotator. Given this information a classifier is trained and new instances for the human annotator to label are selected. This iterative process tries to label as few instances as possible while achieving high classification accuracy.
In this talk I will give a general overview of the core concepts and techniques of active learning like algorithms for selecting the queries and convergence criteria.