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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 Hardcover - 2022

by Masashi Sugiyama; Han Bao; Takashi Ishida

Details

  • Title Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach
  • Author Masashi Sugiyama; Han Bao; Takashi Ishida
  • Binding Hardcover
  • Pages 320
  • Volumes 1
  • Language ENG
  • Publisher MIT Press
  • Date 2022-08-23
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • 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

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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Machine Learning from Weak Supervision

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Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach

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