Publisher's Synopsis
Standard machine learning techniques require large amounts of labelled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labelled data. In this book the authors present theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labelled data. Emphasising an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them.
