Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach

by Masashi Sugiyama

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...

• Format: Hardcover
• Language: English
• ISBN: 0262047071
• ISBN13: 9780262047074
• Release Date: August 2022
• Publisher: MIT Press
• Length: 320 Pages
• Weight: 1.65 lbs.
• Dimensions: 0.7" x 7.0" x 9.1"