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Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach
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Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach (Adaptive Computation and Machine Learning series) Hardcover - 2022

by Sugiyama, Masashi,Bao, Han,Ishida, Takashi,Lu, Nan,Sakai, Tomoya

  • Used
  • very good
  • Hardcover

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The MIT Press, 23/08/2022 00:00:01. hardcover. Very Good. 2.2983 23.3822 18.2861.
Used - Very Good
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Details

  • Title Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach (Adaptive Computation and Machine Learning series)
  • Author Sugiyama, Masashi,Bao, Han,Ishida, Takashi,Lu, Nan,Sakai, Tomoya
  • Binding Hardcover
  • Condition Used - Very Good
  • Pages 320
  • Volumes 1
  • Language ENG
  • Publisher The MIT Press
  • Date 23/08/2022 00:00:01
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # mon0000275356
  • ISBN 9780262047074 / 0262047071
  • Weight 1.65 lbs (0.75 kg)
  • Dimensions 9.1 x 7 x 0.7 in (23.11 x 17.78 x 1.78 cm)
  • Library of Congress subjects Supervised learning (Machine learning)
  • Library of Congress Catalog Number 2021045984
  • Dewey Decimal Code 006.31

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About the author

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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