Table of 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.