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Machine Learning in Non-Stationary EnvironmentsIntroduction to Covariate Shift Adaptation$
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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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Conclusions and Future Prospects

Conclusions and Future Prospects

Chapter:
(p.240) (p.241) 11 Conclusions and Future Prospects
Source:
Machine Learning in Non-Stationary Environments
Author(s):

Masashi Sugiyama

Motoaki Kawanabe

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262017091.003.0011

This chapter summarizes the main themes covered in the preceding discussions and discusses future prospects. This book has provided a comprehensive overview of theory, algorithms, and applications of machine learning under covariate shift. Beyond covariate shift adaptation, it has been shown recently that the ratio of probability densities can be used for solving machine learning tasks. This novel machine learning framework includes multitask learning, privacy-preserving data mining, outlier detection, change detection in time series, two-sample test, conditional density estimation, and probabilistic classification. Furthermore, mutual information—which plays a central role in information theory—can be estimated via density ratio estimation.

Keywords:   machine learning, covariate shift, density ratio estimation, mutual information

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