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図書

Machine learning from weak supervision : an empirical risk minimization approach (Adaptive computation and machine learning series)

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Machine learning from weak supervision : an empirical risk minimization approach

(Adaptive computation and machine learning series)

Call No. (NDL)
M121-D56
Bibliographic ID of National Diet Library
031960278
Material type
図書
Author
Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakayi, and Gang Niu
Publisher
The MIT Press
Publication date
[2022]
Material Format
Paper
Capacity, size, etc.
xv, 295 pages ; 24 cm
NDC
-
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Notes on use

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illustrations (some color)

Detailed bibliographic record

Summary, etc.：

"An overview of machine learning from data that is easily collectible, but challenging to annotate for learning algorithms"--(Provided by: Provided by...

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Paper

Material Type
図書
ISBN
9780262047074 hardcover
0262047071 hardcover
Title
Machine learning from weak supervision : an empirical risk minimization approach
Author/Editor
Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakayi, and Gang Niu
Series Title
Adaptive computation and machine learning series
Title Heading
Adaptive computation and machine learning
Author Heading
杉山, 将 スギヤマ, マサシ ( 01139517 )Authorities
Ishida, Takashi [石田隆]
Sakai, Tomoya [坂井智哉]
Publication, Distribution, etc.
Cambridge, Massachusetts : The MIT Press (出版)
Publication Date
[2022]
Publication Date (W3CDTF)
2022