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Machine learning in non-stationary environments : introduction to covariate shift adaptation

Author: Masashi Sugiyama; Motoaki Kawanabe
Publisher: Cambridge, Mass. : MIT Press, ©2012.
Series: Adaptive computation and machine learning.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:

Theory, algorithms, and applications of machine learning techniques to overcome "covariate shift" non-stationarity.

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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Masashi Sugiyama; Motoaki Kawanabe
ISBN: 9780262017091 0262017091
OCLC Number: 752909553
Description: xiv, 261 pages : illustrations ; 24 cm.
Contents: 1. Introduction and problem formulation --
2. Function approximation --
3. Model selection --
4. Importance estimation --
5. Direct density-ratio estimation with dimensionality reduction --
6. Relation to sample selection bias --
7. Application of covariate shift adaptation --
8. Active learning --
9. Active learning with model selection --
10. Applications of active learning --
11. Conclusions and future prospects.
Series Title: Adaptive computation and machine learning.
Responsibility: Masashi Sugiyama and Motoaki Kawanabe.

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"Though important in practice and conceptually intriguing, the topic of covariate shift adaptation has only recently begun to attract significant attention in machine learning. Building on their Read more...

 
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