Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach

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.

《弱監督分類的基礎理論和實用算法，強調基於經驗風險最小化的方法。》

標準的機器學習技術需要大量標記數據才能運作良好。然而，當我們將機器學習應用於現實世界的問題時，很難收集到如此大量的標記數據。在這本書中，Masashi Sugiyama、Han Bao、Takashi Ishida、Nan Lu、Tomoya Sakai和Gang Niu介紹了弱監督學習的理論和算法，這是一種從弱標記數據中進行機器學習的範式。該書強調基於經驗風險最小化的方法，並借鑒了弱監督學習的最新研究成果，提供了該領域的基礎知識和其背後的高級數學理論。它可以作為從業人員和研究人員的參考書籍，也可以用於教學。

該書首先在數學上對分類問題進行了形式化，定義了常見的符號，並回顧了各種監督二元和多類分類的算法。然後探討了二元弱監督分類的問題，包括正-無標記（PU）分類、正-負-無標記（PNU）分類和無標記-無標記（UU）分類。接著轉向多類分類，討論了互補標籤（CL）分類和部分標籤（PL）分類。最後，該書還涉及更高級的問題，包括一系列改進弱監督學習泛化性能的修正方法和類先驗估計問題。

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.

Masashi Sugiyama是日本理化學研究所先進智能項目中心的主任，也是東京大學的計算機科學教授。Han Bao是東京大學計算機科學系的博士生，同時也是日本理化學研究所先進智能項目中心的研究助理。Takashi Ishida是東京大學的講師，同時也是日本理化學研究所先進智能項目中心的訪問科學家。Nan Lu是東京大學複雜性科學與工程系的博士生，同時也是日本理化學研究所先進智能項目中心的研究助理。Tomoya Sakai是NEC Corporation的高級研究員，同時也是日本理化學研究所先進智能項目中心的訪問科學家。Gang Niu是日本理化學研究所先進智能項目中心的不完全信息學習團隊的研究科學家。