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
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- 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