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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: 30 November 2021

Applications of Active Learning

Applications of Active Learning

Chapter:
(p.225) 10 Applications of Active Learning
Source:
Machine Learning in Non-Stationary Environments
Author(s):

Masashi Sugiyama

Motoaki Kawanabe

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262017091.003.0010

This chapter describes real-world applications of active learning techniques: sampling policy design in reinforcement learning and wafer alignment in semiconductor exposure apparatus.

Keywords:   active learning, sampling policy design, reinforcement learning, wafer alignment

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