Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach

Description: Machine Learning from Weak Supervision by Masashi Sugiyama, Han Bao "An overview of machine learning from data that is easily collectible, but challenging to annotate for learning algorithms"-- FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.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. Author Biography Masashi 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. Table of Contents Preface xiiiI Machine Learning from Weak Supervision1 Introduction 32 Formulation and Notation 213 Supervised Classification 35II Weakly Supervised Learning for Binary Classification4 Positive-Unlabeled (PU) Classification 675 Positive-Negative-Unlabeled (PNU) Classification 856 Positive-Confidence (Pconf) Classification 1117 Pairwise-Constraint Classification 1278 Unlabeled-Unlabeled (UU) Classification 149III Weakly Supervised Learning for Multi-class Classification9 Complementary-Label Classification 17710 Partial-Label Classification 193IV Advanced Topics and Perspectives11 Non-Negative Correction for Weakly Supervised Classification 20712 Class-Prior Estimation 23913 Conclusions and Prospects 275Notes 279Bibliography 283Index 293 Details ISBN0262047071 Author Han Bao Short Title Machine Learning from Weak Supervision Language English Year 2022 ISBN-10 0262047071 ISBN-13 9780262047074 Format Hardcover Subtitle An Empirical Risk Minimization Approach Pages 320 Publisher MIT Press Ltd Series Adaptive Computation and Machine Learning series Publication Date 2022-08-23 Imprint MIT Press Country of Publication United States AU Release Date 2022-08-23 NZ Release Date 2022-08-23 US Release Date 2022-08-23 UK Release Date 2022-08-23 DEWEY 006.31 Audience General We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:136634166;

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