Show Summary Details
- Title Pages
- Adaptive Computation and Machine Learning
- Foreword
- Preface
-
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 Applications of Covariate Shift Adaptation -
8 Active Learning -
9 Active Learning with Model Selection -
10 Applications of Active Learning -
11 Conclusions and Future Prospects -
Appendix: List of Symbols and Abbreviations - Bibliography
- Index
(p.xiii) Preface
(p.xiii) Preface
- Source:
- Machine Learning in Non-Stationary Environments
- Publisher:
- The MIT Press
MIT Press Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.
Please, subscribe or login to access full text content.
If you think you should have access to this title, please contact your librarian.
To troubleshoot, please check our FAQs, and if you can't find the answer there, please contact us.
- Title Pages
- Adaptive Computation and Machine Learning
- Foreword
- Preface
-
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 Applications of Covariate Shift Adaptation -
8 Active Learning -
9 Active Learning with Model Selection -
10 Applications of Active Learning -
11 Conclusions and Future Prospects -
Appendix: List of Symbols and Abbreviations - Bibliography
- Index
