Active learning

Abstract

Active learning is a sub-field of machine learning concerned with how an active learner can make decisions to draw a limited amount of data instances to minimize the generalization error. The increasing digitalization and growing abundance of data make the active learning approach more critical than ever, introducing new challenges across various disciplines. In the chapter, we introduce the field of active learning, recent advances in the field and provide an overview of relevant use cases across several disciplines.