Show Summary Details
- Title Pages
- Series Foreword
- Preface
-
1 When Training and Test Sets Are Different: Characterizing Learning Transfer - Projection and Projectability
-
3 Binary Classification under Sample Selection Bias -
4 On Bayesian Transduction: Implications for the Covariate Shift Problem -
5 On the Training/Test Distributions Gap: A Data Representation Learning Framework -
6 Geometry of Covariate Shift with Applications to Active Learning -
7 A Conditional Expectation Approach to Model Selection and Active Learning under Covariate Shift -
8 Covariate Shift by Kernel Mean Matching -
9 Discriminative Learning under Covariate Shift with a Single Optimization Problem -
10 An Adversarial View of Covariate Shift and a Minimax Approach -
11 Author Comments - References
- Notation and Symbols
- Contributors
- Index
(p.x) (p.xi) Preface
(p.x) (p.xi) Preface
- Source:
- Dataset Shift in Machine Learning
- Publisher:
- The MIT Press
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- Title Pages
- Series Foreword
- Preface
-
1 When Training and Test Sets Are Different: Characterizing Learning Transfer - Projection and Projectability
-
3 Binary Classification under Sample Selection Bias -
4 On Bayesian Transduction: Implications for the Covariate Shift Problem -
5 On the Training/Test Distributions Gap: A Data Representation Learning Framework -
6 Geometry of Covariate Shift with Applications to Active Learning -
7 A Conditional Expectation Approach to Model Selection and Active Learning under Covariate Shift -
8 Covariate Shift by Kernel Mean Matching -
9 Discriminative Learning under Covariate Shift with a Single Optimization Problem -
10 An Adversarial View of Covariate Shift and a Minimax Approach -
11 Author Comments - References
- Notation and Symbols
- Contributors
- Index
