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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
(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
