Description: Machine Learning from Weak Supervision : An Empirical Risk Minimization Approach, Hardcover by Sugiyama, Masashi; Bao, Han; Ishida, Takashi; Lu, Nan; Sakai, Tomoya, ISBN 0262047071, ISBN-13 9780262047074, Brand New, Free shipping in the US 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, th 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 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, th 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.
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Book Title: Machine Learning from Weak Supervision : An Empirical Risk Minimi
Number of Pages: 320 Pages
Publication Name: Machine Learning from Weak Supervision : an Empirical Risk Minimization Approach
Language: English
Publisher: MIT Press
Item Height: 0.8 in
Publication Year: 2022
Subject: Game Theory, Programming / Algorithms, Intelligence (Ai) & Semantics, General
Item Weight: 26.3 Oz
Type: Textbook
Subject Area: Mathematics, Computers, Science
Item Length: 9.3 in
Author: Tomoya Sakai, Han Bao, Masashi Sugiyama, Nan Lu, Takashi Ishida
Item Width: 7.2 in
Series: Adaptive Computation and Machine Learning Ser.
Format: Hardcover
