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Machine Learning in Non-Stationary EnvironmentsIntroduction to Covariate Shift Adaptation$
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Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

Masashi Sugiyama and Motoaki Kawanabe

Print publication date: 2012

Print ISBN-13: 9780262017091

Published to MIT Press Scholarship Online: September 2013

DOI: 10.7551/mitpress/9780262017091.001.0001

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PRINTED FROM MIT PRESS SCHOLARSHIP ONLINE (www.mitpress.universitypressscholarship.com). (c) Copyright The MIT Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 13 June 2021

Relation to Sample Selection Bias

Relation to Sample Selection Bias

Chapter:
(p.124) (p.125) 6 Relation to Sample Selection Bias
Source:
Machine Learning in Non-Stationary Environments
Author(s):

Masashi Sugiyama

Motoaki Kawanabe

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262017091.003.0006

This chapter compares the covariate shift approach with related formulations called sample selection bias. Studies of correcting sample selection bias were initiated by Heckman, who received the Nobel Prize in economics for this achievement in 2000. Heckman’s correction model is reviewed and its relation to covariate shift adaptation is discussed.

Keywords:   covariate shift adaptation, sample selection model, Heckman, correction model

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