Machine Learning from Weak Supervision Book Reviews
Machine Learning from Weak Supervision by Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu & Tomoya Sakai Book Summary
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.
Book Name | Machine Learning from Weak Supervision |
Genre | Mathematics |
Author | Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu & Tomoya Sakai |
Published | 23 August 2022, Tuesday |
Language | English |
E-Book Size | 26.88 MB |
Machine Learning from Weak Supervision (Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu & Tomoya Sakai) Book Reviews 2024
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Summary of Machine Learning from Weak Supervision by Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu & Tomoya Sakai
The Machine Learning from Weak Supervision book written by Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu & Tomoya Sakai was published on 23 August 2022, Tuesday in the Mathematics category. A total of 170 readers of the book gave the book 0 points out of 5.
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