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Contents

Preface

Acknowledgments

1 Automating Inquiry

1.1 A Thought Experiment

1.2 Active Learning

1.3 Scenarios for Active Learning

2 Uncertainty Sampling

2.1 Pushing the Boundaries

2.2 An Example

2.3 Measures of Uncertainty

2.4 Beyond Classification

2.5 Discussion

3 Searching Through the Hypothesis Space

3.1 The Version Space

3.2 Uncertainty Sampling as Version Space Search

3.3 Query by Disagreement

3.4 Query by Committee

3.5 Discussion

4 Minimizing Expected Error and Variance

4.1 Expected Error Reduction

4.2 Variance Reduction

4.3 Batch Queries and Submodularity

4.4 Discussion

5 Exploiting Structure in Data

5.1 Density-Weighted Methods

5.2 Cluster-Based Active Learning

5.3 Active + Semi-Supervised Learning

5.4 Discussion

6 Theory

6.1 A Unified View

6.2 A PAC Bound for Active Learning

6.3 Discussion

7 Practical Considerations

7.1 Which Algorithm is Best?

7.2 Real Labeling Costs

7.3 Alternative Query Types

7.4 Skewed Label Distributions

7.5 Unreliable Oracles

7.6 Multi-Task Active Learning

7.7 Data Reuse and the Unknown Model Class

7.8 Stopping Criteria

A Nomenclature Reference

Bibliography

Author’s Biography

Index