Active Learning

1. Front Matter

   Pages i-xiv

   PDF

2. Automating Inquiry

   • Burr Settles

   Pages 1-9

3. Uncertainty Sampling

   • Burr Settles

   Pages 11-20

4. Searching Through the Hypothesis Space

   • Burr Settles

   Pages 21-35

5. Minimizing Expected Error and Variance

   • Burr Settles

   Pages 37-46

6. Exploiting Structure in Data

   • Burr Settles

   Pages 47-54

7. Theory

   • Burr Settles

   Pages 55-62

8. Practical Considerations

   • Burr Settles

   Pages 63-77

9. Back Matter

   Pages 79-100

   PDF

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. Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical Considerations

• Carnegie Mellon University, USA

  Burr Settles

Burr Settles leads the research group at Duolingo, an award-winning website and mobile app offering free language education for the world. He also runs FAWM.ORG, a global annual songwriting experiment. His research has been published in NIPS, ICML, AAAI, ACL, EMNLP, NAACL-HLT, and CHI, and has been covered by The New York Times, Slate, Forbes, WIRED, and the BBC among others. In past lives, he was a postdoc at Carnegie Mellon and earned a PhD from UW-Madison.