Active learning
- Responsibility
- Burr Settles.
- Imprint
- Cham, Switzerland : Springer, ©2012.
- Physical description
- 1 online resource (xiii, 100 pages) : illustrations
- Series
- Synthesis lectures on artificial intelligence and machine learning ; #18. 1939-4616
Online
Available online
More options
Description
Creators/Contributors
- Author/Creator
- Settles, Burr.
Contents/Summary
- Bibliography
- Includes bibliographical references (pages 81-96) and index.
- Contents
-
- Automating Inquiry Uncertainty Sampling Searching Through the Hypothesis Space Minimizing Expected Error and Variance Exploiting Structure in Data Theory Practical Considerations.
- (source: Nielsen Book Data)
- Publisher's 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.
(source: Nielsen Book Data)
Subjects
- Subjects
- Supervised learning (Machine learning)
- Explanation-based learning.
- Apprentissage supervisé (Intelligence artificielle)
- Apprentissage par explication (Intelligence artificielle)
- COMPUTERS > Enterprise Applications > Business Intelligence Tools.
- COMPUTERS > Intelligence (AI) & Semantics.
- active learning
- expected error reduction
- hierarchical sampling
- optimal experimental design
- query by committee
- query by disagreement
- query learning
- uncertainty sampling
- variance reduction
Bibliographic information
- Publication date
- 2012
- Series
- Synthesis lectures on artificial intelligence and machine learning, 1939-4616 ; #18
- Note
- Part of: Synthesis digital library of engineering and computer science.
- Referenced in
- Compendex
- INSPEC
- Google scholar
- Google book search
- ISBN
- 9781608457267 (electronic bk.)
- 1608457265 (electronic bk.)
- 9781608457250 (pbk.)
- 9783031015601 (electronic bk.)
- 3031015606 (electronic bk.)
- DOI
- 10.2200/S00429ED1V01Y201207AIM018
- 10.1007/978-3-031-01560-1
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