| 청구기호 |
Q325.75 .S472 2012 |
| 형태사항 |
1 electronic text (xiii, 100 p.) : ill., digital file.
|
| 언어 |
English |
| 일반주기 |
Part of: Synthesis digital library of engineering and computer science.
Series from website.
|
| 서지주기 |
Includes bibliographical references (p. 81-96) and index.
|
| 내용 |
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.
|
| 주제 |
Supervised learning (Machine learning)
Explanation-based learning.
active learning
expected error reduction
hierarchical sampling
optimal experimental design
query by committee
query by disagreement
query learning
uncertainty sampling
variance reduction
|
| ISBN |
9781608457267 (electronic bk.)
|
| 기타 표준번호 |
10.2200/S00429ED1V01Y201207AIM018 |
