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The MIT Press, 23/08/2022 00:00:01. hardcover. Very Good. 2.2983 23.3822 18.2861.
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Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach Hardcover - 2022
by Masashi Sugiyama; Han Bao; Takashi Ishida
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- 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
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Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach (Adaptive Computation and Machine Learning series)
by Sugiyama, Masashi,Bao, Han,Ishida, Takashi,Lu, Nan,Sakai, Tomoya
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Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach (Adaptive Computation and Machine Learning series)
by Sugiyama, Masashi, Bao, Han, Ishida, Takashi, Lu, Nan, Sakai
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Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach (Adaptive Computation and Machine Learning series)
by Sugiyama, Masashi; Bao, Han; Ishida, Takashi; Lu, Nan; Sakai, Tomoya
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Machine Learning From Weak Supervision
by Masashi Sugiyama, Han Bao
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Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach
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Machine Learning from Weak Supervision
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Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach (Adaptive Computation and Machine Learning series)
by Sugiyama, Masashi
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Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach
by Sugiyama, Masashi/ Bao, Han/ Ishida, Takashi/ Lu, Nan/ Sakai, Tomoya
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Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach (Adaptive Computation and Machine Learning series)
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