Machine Learning from Weak Supervision
An Empirical Risk Minimization Approach
$172.52
- Hardcover
320 pages
- Release Date
5 October 2022
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 alg…
Book Details
| ISBN-13: | 9780262047074 |
|---|---|
| ISBN-10: | 0262047071 |
| Author: | Masashi Sugiyama, Han Bao |
| Publisher: | MIT Press Ltd |
| Imprint: | MIT Press |
| Format: | Hardcover |
| Number of Pages: | 320 |
| Release Date: | 5 October 2022 |
| Weight: | 567g |
| Dimensions: | 229mm x 178mm |
| Series: | Adaptive Computation and Machine Learning series |
About The Author
Masashi Sugiyama
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.
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