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  • © 2012

Active Learning

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  • ISBN: 978-3-031-01560-1
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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xiv
  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

About this book

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

Authors and Affiliations

  • Carnegie Mellon University, USA

    Burr Settles

About the author

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.

Bibliographic Information

Buying options

eBook USD 29.99
Price excludes VAT (USA)
  • ISBN: 978-3-031-01560-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 39.99
Price excludes VAT (USA)