Limit search to available items
Book Cover
Author Settles, Burr.

Title Active learning / Burr Settles
Published San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, [2012]
Online access available from:
Synthesis Digital Library    View Resource Record  


Description 1 online resource (xiii, 100 pages) : illustrations
Series Synthesis lectures on artificial intelligence and machine learning, 1939-4616 ; #18
Synthesis lectures on artificial intelligence and machine learning ; #18. 1939-4616
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
Summary The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data instances to be labeled by an "oracle" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or "query selection frameworks."We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities
Analysis active learning
expected error reduction
hierarchical sampling
optimal experimental design
query by committee
query by disagreement
query learning
uncertainty sampling
variance reduction
Notes Part of: Synthesis digital library of engineering and computer science
Bibliography Includes bibliographical references (pages 81-96) and index
Notes Online resource; title from PDF title page (Morgan & Claypool, viewed Sept. 27, 2012)
Subject Explanation-based learning.
Supervised learning (Machine learning)
Form Electronic book
ISBN 1608457265 (electronic bk.)
9781608457267 (electronic bk.)