Coverart for item
The Resource Dataset shift in machine learning, [edited by] Joaquin Quiñonero-Candela [and others]

Dataset shift in machine learning, [edited by] Joaquin Quiñonero-Candela [and others]

Label
Dataset shift in machine learning
Title
Dataset shift in machine learning
Statement of responsibility
[edited by] Joaquin Quiñonero-Candela [and others]
Contributor
Subject
Language
eng
Summary
This work is an overview of recent efforts in the machine learning community to deal with dataset and covariate shift which occurs when test and training inputs and outputs have different distributions
Member of
Action
digitized
Dewey number
006.3/1
Illustrations
illustrations
Index
index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
Quiñonero-Candela, Joaquin
Series statement
Neural information processing series
http://library.link/vocab/subjectName
Machine learning
Label
Dataset shift in machine learning, [edited by] Joaquin Quiñonero-Candela [and others]
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references (pages 207-218) and index
Color
multicolored
Contents
  • II.
  • Theoretical views on dataset and covariate shift
  • 3.
  • Binary classification under sample selection bias
  • Matthias Hein
  • 4.
  • On Bayesian transduction: implications for the covariate shift problem
  • Lars Kai Hansen
  • 5.
  • On the training/test distributions gap: a data representation learning framework
  • I.
  • Shai Ben-David
  • III.
  • Algorithms for covariate shift
  • 6.
  • Geometry of covariate shift with applications to active learning
  • Takafumi Kanamori and Hidetoshi Shimodaira
  • 7.
  • A conditional expectation approach to model selection and active learning under covariate shift
  • Masashi Sugiyama, Neil Rubens and Klaus-Robert Muller
  • 8.
  • Introduction to dataset shift
  • Covariate shift by kernel mean matching
  • Arthur Grellon, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt and Bernhard Scholkopf
  • 9.
  • Discriminative learning under covariate shift with a single optimization problem
  • Steffen Bickel, Michael Bruckner and Tobias Scheffer
  • 10.
  • An adversarial view of covariate shift and a minimax approach
  • Amir Globerson, Choon Hui Teo, Alex Smola and Sam Roweis
  • IV.
  • Discussion
  • 1.
  • 11.
  • Author comments
  • Hidetoshi Shimodaira, Masashi Sugiyama, Amos Storkey, Arthur Gretton and Shai-Ben David
  • When training and test sets are different: characterizing learning transfer
  • Amos Storkey
  • 2.
  • Projection and projectability
  • David Corfield
Control code
ocn310915974
Dimensions
unknown
Extent
1 online resource (xv, 229 pages)
File format
unknown
Form of item
online
Isbn
9780262255103
Level of compression
unknown
Other control number
9786612240386
Other physical details
illustrations
Quality assurance targets
not applicable
Reformatting quality
unknown
Reproduction note
Electronic reproduction.
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)310915974
System details
Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.
Label
Dataset shift in machine learning, [edited by] Joaquin Quiñonero-Candela [and others]
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references (pages 207-218) and index
Color
multicolored
Contents
  • II.
  • Theoretical views on dataset and covariate shift
  • 3.
  • Binary classification under sample selection bias
  • Matthias Hein
  • 4.
  • On Bayesian transduction: implications for the covariate shift problem
  • Lars Kai Hansen
  • 5.
  • On the training/test distributions gap: a data representation learning framework
  • I.
  • Shai Ben-David
  • III.
  • Algorithms for covariate shift
  • 6.
  • Geometry of covariate shift with applications to active learning
  • Takafumi Kanamori and Hidetoshi Shimodaira
  • 7.
  • A conditional expectation approach to model selection and active learning under covariate shift
  • Masashi Sugiyama, Neil Rubens and Klaus-Robert Muller
  • 8.
  • Introduction to dataset shift
  • Covariate shift by kernel mean matching
  • Arthur Grellon, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt and Bernhard Scholkopf
  • 9.
  • Discriminative learning under covariate shift with a single optimization problem
  • Steffen Bickel, Michael Bruckner and Tobias Scheffer
  • 10.
  • An adversarial view of covariate shift and a minimax approach
  • Amir Globerson, Choon Hui Teo, Alex Smola and Sam Roweis
  • IV.
  • Discussion
  • 1.
  • 11.
  • Author comments
  • Hidetoshi Shimodaira, Masashi Sugiyama, Amos Storkey, Arthur Gretton and Shai-Ben David
  • When training and test sets are different: characterizing learning transfer
  • Amos Storkey
  • 2.
  • Projection and projectability
  • David Corfield
Control code
ocn310915974
Dimensions
unknown
Extent
1 online resource (xv, 229 pages)
File format
unknown
Form of item
online
Isbn
9780262255103
Level of compression
unknown
Other control number
9786612240386
Other physical details
illustrations
Quality assurance targets
not applicable
Reformatting quality
unknown
Reproduction note
Electronic reproduction.
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)310915974
System details
Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.

Library Locations

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      Albany, Auckland, 0632, NZ
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