Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach Sugiyama Masashi
Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach Sugiyama Masashi Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an…
Specifikacia Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach Sugiyama Masashi
Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach Sugiyama Masashi
Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Standard machine learning techniques require large amounts of labeled data to work well. This book presents theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data.
It can be used as a reference for practitioners and researchers and in the classroom.The book first mathematically formulates Emphasizing 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.