Active learning /
| Main Author: | |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) :
Morgan & Claypool,
[2012]
|
| Series: | Synthesis lectures on artificial intelligence and machine learning ;
#18. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
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.