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. When we apply machine learning to problems in the
physical world, however, it is extremely difficult to collect such
quantities of labeled data. In this book Masashi Sugiyama, Han Bao,
Takashi Ishida, Nan Lu, Tomoya Sakai and Gang Niu present theory and
algorithms for weakly supervised learning, a paradigm of machine
learning from weakly labeled data. 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. It can
be used as a reference for practitioners and researchers and in the
classroom.
The book first mathematically formulates classification problems,
defines common notations, and reviews various algorithms for supervised
binary and multiclass classification. It then explores problems of
binary weakly supervised classification, including positive-unlabeled
(PU) classification, positive-negative-unlabeled (PNU) classification,
and unlabeled-unlabeled (UU) classification. It then turns to multiclass
classification, discussing complementary-label (CL) classification and
partial-label (PL) classification. Finally, the book addresses more
advanced issues, including a family of correction methods to improve the
generalization performance of weakly supervised learning and the problem
of class-prior estimation.
