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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, 2022. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 23 May 2022

Applications of Covariate Shift Adaptation

Applications of Covariate Shift Adaptation

Chapter:
(p.137) 7 Applications of Covariate Shift Adaptation
Source:
Machine Learning in Non-Stationary Environments
Author(s):

Masashi Sugiyama

Motoaki Kawanabe

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262017091.003.0007

This chapter discusses state-of-the-art applications of covariate shift adaptation techniques to various real-world problems. It covers non-stationarity adaptation in brain-computer interfaces; speaker identification through change in voice quality; domain adaptation in natural language processing; age prediction from face images under changing illumination conditions; user adaptation in human activity recognition; and efficient sample reuse in autonomous robot control.

Keywords:   covariate shift adaptation, brain-computer interface, speaker identification, domain adaptation, natural language processing, age prediction, user adaptation, autonomous robot control

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