{"product_id":"machine-learning-from-weak-supervision-isbn-9780262047074","title":"Machine Learning from Weak Supervision","description":"\u003cb\u003eFundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eStandard 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.\u003cbr\u003e\u003cbr\u003eThe 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.Preface xiii\u003cbr\u003eI Machine Learning from Weak Supervision\u003cbr\u003e1 Introduction 3\u003cbr\u003e2 Formulation and Notation 21\u003cbr\u003e3 Supervised Classification 35\u003cbr\u003eII Weakly Supervised Learning for Binary Classification\u003cbr\u003e4 Positive-Unlabeled (PU) Classification 67\u003cbr\u003e5 Positive-Negative-Unlabeled (PNU) Classification 85\u003cbr\u003e6 Positive-Confidence (Pconf) Classification 111\u003cbr\u003e7 Pairwise-Constraint Classification 127\u003cbr\u003e8 Unlabeled-Unlabeled (UU) Classification 149\u003cbr\u003eIII Weakly Supervised Learning for Multi-class Classification\u003cbr\u003e9 Complementary-Label Classification 177\u003cbr\u003e10 Partial-Label Classification 193\u003cbr\u003eIV Advanced Topics and Perspectives\u003cbr\u003e11 Non-Negative Correction for Weakly Supervised Classification 207\u003cbr\u003e12 Class-Prior Estimation 239\u003cbr\u003e13 Conclusions and Prospects 275\u003cbr\u003eNotes 279\u003cbr\u003eBibliography 283\u003cbr\u003eIndex 293Masashi Sugiyama is Director of the RIKEN Center for Advanced Intelligence Project and Professor of Computer Science at the University of Tokyo. Han Bao is a PhD student in the Department of Computer Science at the University of Tokyo and Research Assistant at the RIKEN Center for Advanced Intelligence Project. Takashi Ishida is a Lecturer at the University of Tokyo and Visiting Scientist at the RIKEN Center for Advanced Intelligence Project. Nan Lu is a PhD student in the Department of Complexity Science and Engineering at the University of Tokyo and Research Assistant at the RIKEN Center for Advanced Intelligence Project. Tomoya Sakai is Senior Researcher at NEC Corporation and Visiting Scientist at the RIKEN Center for Advanced Intelligence Project. Gang Niu is Research Scientist in the Imperfect Information Learning Team at the RIKEN Center for Advanced Intelligence Project.","brand":"The MIT Press","offers":[{"title":"Default Title","offer_id":46302460641509,"sku":"NP9780262047074","price":65.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780262047074.jpg?v=1767732012","url":"https:\/\/k12savings.com\/es\/products\/machine-learning-from-weak-supervision-isbn-9780262047074","provider":"K12savings","version":"1.0","type":"link"}