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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


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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