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When Training and Test Sets Are Different: Characterizing Learning Transfer

Storkey Amos

in Dataset Shift in Machine Learning

Published in print:
2008
Published Online:
August 2013
ISBN:
9780262170055
eISBN:
9780262255103
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262170055.003.0001
Subject:
Computer Science, Machine Learning

This chapter introduces the general learning transfer problem and formulates it in terms of a change of scenario. Standard regression and classification models can be characterized as conditional ... More


Dataset Shift in Machine Learning

Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence (eds)

Published in print:
2008
Published Online:
August 2013
ISBN:
9780262170055
eISBN:
9780262255103
Item type:
book
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262170055.001.0001
Subject:
Computer Science, Machine Learning

Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of ... More


On Bayesian Transduction: Implications for the Covariate Shift Problem

Kai Hansen Lars

in Dataset Shift in Machine Learning

Published in print:
2008
Published Online:
August 2013
ISBN:
9780262170055
eISBN:
9780262255103
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262170055.003.0004
Subject:
Computer Science, Machine Learning

This chapter analyzes Bayesian supervised learning with extensions to semisupervised learning, and learning with covariate or dataset shift. The main result is an expression for the generalization ... More


Author Comments

Shimodaira Hidetoshi, Sugiyama Masashi, Storkey Amos, Gretton Arthur, and David Shai-Ben

in Dataset Shift in Machine Learning

Published in print:
2008
Published Online:
August 2013
ISBN:
9780262170055
eISBN:
9780262255103
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262170055.003.0011
Subject:
Computer Science, Machine Learning

This chapter presents comments by the authors about dataset shift in machine learning. Topics covered include covariate shift and misspecification; and whether importance weighting is needed under ... More


Projection and Projectability

Corfield David

in Dataset Shift in Machine Learning

Published in print:
2008
Published Online:
August 2013
ISBN:
9780262170055
eISBN:
9780262255103
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262170055.003.0002
Subject:
Computer Science, Machine Learning

This chapter shows how the problem of dataset shift has been addressed by different philosophical schools under the concept of “projectability.” When philosophers tried to formulate scientific ... More


On the Training/Test Distributions Gap: A Data Representation Learning Framework

Ben-David Shai

in Dataset Shift in Machine Learning

Published in print:
2008
Published Online:
August 2013
ISBN:
9780262170055
eISBN:
9780262255103
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262170055.003.0005
Subject:
Computer Science, Machine Learning

This chapter discusses some dataset shift learning problems from a formal, statistical point of view. It provides definitions for “multitask learning,” “inductive transfer,” and “domain adaptation,” ... More


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